Executive Profiles

Committed to transforming the future of transportation.

Over the last decade, Jiji has led Acsia’s growth from a fledgling startup to a global leader in automotive software. He continues to be responsible for setting the organization’s strategic direction and business goals.

With over 25 years of experience leading OEM production programs for marquee brands like BMW, Mercedes-Benz, and Nissan, and Tier-I suppliers like Mitsubishi Electric and Panasonic, Jiji is an expert in the areas of embedded systems, and automation across automotive and industrial domains.

Jiji holds a graduate (B.S.) degree in information technology and postgraduate (M.S.) degree in software systems from the Birla Institute of Technology and Science (BITS) Pilani (Rajasthan) and has completed an executive management program from the Stanford Graduate School of Business, California.

In 2025, Rejeesh was entrusted with the onerous responsibility of incubating new business opportunities to propel Acsia’s growth. With Acsia since 2015, Rejeesh has held various roles in the delivery organization till 2021 before being entrusted to lead pre-sales and solutions. He was responsible for collaborating with internal stakeholders to craft compelling solutions and build winning proposals.

With over 25 years of experience leading OEM production programs for brands like Mercedes-Benz, and Tier-I suppliers like Mitsubishi Electric and Visteon, Rejeesh is an expert in understanding customer business needs and proposing technology investments needed to realize revenue goals.

Rejeesh holds a graduate degree (B.S.) in information systems from the Birla Institute of Technology and Science (BITS) Pilani (Rajasthan).

Since 2024, Anil has led Acsia’s global engineering organization focusing on technical strategy and solution development. Prior to this, Anil led Acsia’s global delivery engine since 2017, ensuring quality, time to market, and compliance. He was responsible for full project life cycles including requirements gathering, creation of project plans and schedules, identifying and deploying resources, managing budgets, and facilitating project execution till closure.

With over 25 years of experience leading OEM production programs for iconic customers like BMW, Skoda, Volkswagen, Mercedes-Benz, and Ford, and Tier-I suppliers like JoyNext and Garmin, Anil is an expert in embedded systems, AUTOSAR, Android Automotive, ASPICE, Cybersecurity and Functional Safety (FuSa).

Anil holds a postgraduate (M.C.A.) degree in computer science from the SRM College of Engineering, Chennai (Tamil Nadu).

In a career spanning 27+ years, Jayachandran has garnered extensive experience in HR service delivery, talent acquisition and development, mentoring, performance-productivity management, coaching, career succession planning, employee engagement, competency development, office administration, statutory compliance, and payroll management.

At Acsia, Jayachandran is responsible for all people-related policies, processes, and practices. Under his able leadership, Acsia has been certified as a Great Place To Work (GPTW).

Jayachandran holds a postgraduate degree (M.Sc.) in physics from the Barkatullah University Bhopal (Madhya Pradesh).

For over 25 years, Vasantharaj has led projects delivering software design and development for the automotive domain. His expertise spans Android, Linux, Embedded Linux, Solaris, HP-UX, and Windows systems, C/C++, hypervisors and RTOS, abstraction layers, application layer components, middleware layers, kernel modules, and ASPICE.

At Acsia since 2014 as one of the founding team members, Vasantharaj has been instrumental in architecting innovative solutions for major projects like rear-seat entertainment middleware, instrument cluster development, and head unit system for Android Automotive-based infotainment.

With experience managing OEM production programs for brands like Volkswagen, Porshe, Audi, and Mercedes-Benz, and Tier-I suppliers like Panasonic and JoyNext, Vasantharaj leads our innovation engine.

Vasantharaj holds a diploma in electrical engineering from the Kerala State Board of Technical Education and a post-graduate diploma in computer science from the Electronics Research & Development Centre of India (ER&DCI), a part of the Centre for Development of Advanced Computing (C-DAC), Thiruvananthapuram (Kerala).

Datta became a part of the Acsia family in March 2023 post the acquisition of Arctictern Solutions GmbH, a business he founded. He brings over 26 years of experience of which 16 have been spent in the automotive industry spanning functions like sales, procurement, mergers & acquisitions, and operational excellence.

At a leading connected car technology provider, Datta built offshore delivery centers in Ukraine, Romania, China, and India and led several mergers & acquisitions, and divestments. At one of the foremost automotive tier-I corporations, he was responsible for indirect procurement globally and managed their $150 million savings initiative across engineering programs.

Datta holds a graduate (B.E.) degree in mechanical engineering and an M.B.A., both from Karnatak University, Dharwad (Karnataka).

With more than three decades of experience across the automotive, aerospace and defence sectors, Lutz Nettig leads Acsia’s European sales strategy and business development as Head of Sales and Business Development, Europe. He is responsible for driving partnerships and expanding the company’s presence across the region.

Before joining Acsia, Lutz held senior leadership roles at leading software companies serving the automotive industry, where he built and scaled operations, developed strategic partnerships and managed full P&L responsibilities for businesses exceeding €100 million in revenue. Over his career he has helped OEMs and Tier-1 suppliers deliver complex engineering solutions, from transforming global OEM accounts for end-to-end ADAS and Car-to-X projects to building supplier relationships with multiple global firms.

Lutz holds a Diplom-Ingenieur (Master’s equivalent) in Electrical Engineering and Electronics from the Frankfurt University of Applied Sciences.

Atsushi has been with Acsia since 2015 and manages key relationships with OEMs and Tier-I suppliers in Japan. A Japanese automotive industry veteran, Atsushi brings over 38 years of experience at a leading Japanese Tier-I supplier managing production programs related to car audio hardware development and automotive software for the world’s leading automobile manufacturers.

Atsushi holds a graduate (B.S.) degree in electrical engineering from Yamaguchi University, Japan.

With Acsia since 2015, Nibil has been a core member of our Advanced Technology Group and instrumental in architecting innovative solutions for various customer products. He comes with more than 20 years of experience in the design and development of automotive systems for the instrument cluster, infotainment, body, and power train domains.

With experience leading OEM production programs for customers like BMW, Mercedes-Benz, General Motors, Volvo, Mahindra, and Tata Motors, and Tier-I suppliers like Panasonic, Garmin and Aptiv, Nibil’s expertise spans single and multi-core micro-controllers and micro-processor-based software architecture using AUTOSAR, Android, Linux, hypervisors and containers, system software design of multi-node automotive systems, and protocols like SPI, SCI, I2C, CAN, LIN, J1939, KWP, UDS, ISO TP, and APIX.

Nibil holds a graduate (B.Tech.) degree in computer science and engineering from the MES Engineering College, Malappuram (Kerala), and is a certified ISO 26262 Functional Safety Engineer.

Sojan brings over 25 years of experience in software development, system, and software architecture for embedded and automotive systems. A “full stack” engineer, he has excellent understanding of complex hardware and software systems, and expertise across Linux and QNX Operating Systems and system software development in C, Rust and C++.

He strongly believes that automotive and embedded system industries should embrace modern languages for system development and that led Sojan to develop Sabaton, a Rust programming language-based automotive Linux software platform.

With experience leading OEM production programs for customers like BMW, Mercedes-Benz, General Motors, Stellantis, Toyota, and Tier-I suppliers like Harman, Sojan is an industry expert in automotive infotainment, telematics, and embedded systems.

Sojan holds a graduate degree (B.E.) in electrical, electronics and communications engineering from the Bangalore Institute of Technology, Bangalore (Karnataka).

Since 2023 with Acsia, Diljith has helped develop and take to market the Telematics and V2X solutions suite. In a career spanning over 23 years in ECU Systems and Software development for Telematics, V2X & Connected technologies, Infotainment, and Safety & Body control domains, he has worn multiple hats – SME, Systems architect, and Advanced technical lead.

With extensive production program experience across OEMs like BMW, Volvo, General Motors, SVW, Renault, Ford, Mazda, and Fiat-Chrysler Automobiles and Tier-1s like Lear, Visteon, Aptiv, HSAE and LG, Diljith has held responsibilities for driving the system design, technical concepts and system architectures for car and truck platforms.

Diljith holds a graduate (B.E.) degree in electrical and electronics engineering from Bangalore University.

With Acsia since 2016, Gloria has successfully led large engagements with major automotive OEMs and Tier-I suppliers across the U.S., E.U., and Japan adhering to ASPICE, ISO and CMMi

Level 5 standards. She comes with more than 20 years of experience in automotive and embedded system software development with deep expertise in HMI and middleware application and cybersecurity implementation including threat analysis for IVI and cluster software.

Armed with the experience of leading OEM production programs for customers like BMW, Mercedes-Benz, Volkswagen, Skoda, Volvo, Honda, Ola, and Tier-I suppliers like Panasonic, in 2024 Gloria transitioned to head Acsia’s Resource Management Group responsible for ensuring skilled talent is always available for deployment and their optimum utilization.

Gloria holds a diploma in computer science from the Kerala Government Polytechnic College Kalamassery (Kerala) and a postgraduate degree in computer application (M.C.A.) from the Sikkim Manipal University, Manipal (Karnataka).

Rajan brings over 20 years of experience in project delivery across after-sales, vehicle production systems, logistics, supply chain management, and infotainment. A PMP and ACP (Agile) certified professional, he serves as a Delivery Leader at Acsia, with deep expertise in project management and Scrum methodologies, and a proven ability to lead complex programs from initiation to closure.

With experience leading OEM production programs for customers such as Nissan, Ford, BMW, and Mercedes-Benz, as well as Tier-1 suppliers including Visteon, IAV, and Panasonic, Rajan is recognised for his strong track record in automotive project leadership.

Rajan holds a graduate (B.E.) degree in Instrumentation & Control from the University of Madras and has completed an executive program in Logistics, Materials, and Supply Chain Management from the Xavier School of Management.

Since joining Acsia in 2018, Valliaappan has been leading testing programs with a focus on quality, process improvement, and global delivery. A PRINCE2 and QAI-certified Test Manager, he brings over 20 years of experience spanning the automotive, information technology, and services industries.

Throughout his career, he has taken on diverse responsibilities – test management, project and vendor management, test automation, and people upskilling – while ensuring adherence to ASPICE and established quality processes.

With production program experience across OEMs such as Mercedes-Benz, BMW, Ford, and Tata, and Tier-1 suppliers including Panasonic, GARMIN, Visteon, and Bestec, Valliaappan has successfully coordinated test activities across multiple geographies.

Valliaappan holds a graduate (B.E.) degree in Mechanical Engineering from Kumaraguru College of Technology.

Jose brings over 25 years of experience in the information technology industry in marketing leadership roles across some of the most reputed names in IT consulting & services, and SaaS. He has established and scaled marketing and communications functions supporting business development and sales efforts in markets like the United States, the United Kingdom, Germany, France, Sweden, the Netherlands, India, and the Middle East.

His deep expertise in full-funnel marketing spans branding, media & analyst relations, events, account-based marketing, pursuit marketing, and content marketing. At Acsia, he is responsible for driving brand awareness, lead generation, fortifying key account relationships, enhancing win rates, and strengthening the employer brand.

Jose holds a graduate degree in management from Christ University, Bengaluru (Karnataka) and a postgraduate degree in communications from MKU University, Madurai (Tamil Nadu). He has also completed an executive program in strategic marketing management from the Stanford Graduate School of Business.

Ratish is a seasoned professional with over 15 years of experience in global sales, business development, and key account management across high-tech industries, including automotive, semiconductors, manufacturing, and healthcare, in markets like the United States, India and Asia Pacific (APAC) region.

With more than 8 years of experience at leading automotive tier-1 suppliers later acquired by OEMs, Ratish comes with deep knowledge of automotive software and electronics product development from concept to production in infotainment, ECU and e-Powertrain. At Acsia, he is responsible for building a high-performance sales team and leading the customer acquisition strategy for India and the APAC region.

Ratish holds a graduate degree in electronics engineering from Pune University.

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AH2025/PS06 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

 

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

 

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

 

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

 

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

 

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS05 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS04 | AI/ML

Context

Software teams struggle to diagnose system failures from massive log files. Manual analysis is slow, error-prone, and requires expert knowledge. Root cause extraction from unstructured, noisy logs. Use creative algorithms, LLM prompting strategies, or hybrid heuristics.

Pain Point

  • Manual log analysis is slow, error-prone, and requires deep expertise in both the system and its environment.
  • Critical issues can be missed or misdiagnosed, leading to longer downtimes and higher costs.
  • Existing monitoring tools often raise alerts without actionable insights, leaving developers to do the heavy lifting.

Challenge

Build an AI-powered log analytics assistant that can:

  • Ingest and parse unstructured application logs at scale.
  • Automatically flag potential defects or anomalies.
  • Summarize possible root causes in natural language.
  • Provide actionable insights that developers can use immediately.

Goal

Deliver a working prototype that:

  • Operates on sample log data.
  • Produces insights that are accurate, usable, and easy to interpret.
  • Bridges the gap between raw log data and developer-friendly diagnostics.

Outputs

  • Automated defect detection (flagging anomalies in logs).
  • Root cause summaries in natural language.
  • Actionable recommendations (e.g., suspected component failure, probable misconfiguration).
  • Visualization/dashboard (if possible) for quick triage.

Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
  • Increased developer productivity, freeing engineers to focus on fixes rather than sifting logs.
  • Improved reliability of complex software systems.
  • Scalable approach that can be extended across industries (finance, automotive, telecom, healthcare).
AH2025/PS03 | AI/ML

Context

Drivers and passengers spend significant time in vehicles where comfort, safety, and accessibility directly affect satisfaction and well-being. Yet today’s in-car systems remain largely static and manual, requiring users to adjust climate, seats, infotainment, and navigation themselves. With increasing connectivity, AI offers the potential to transform cars into adaptive, intelligent companions.

Pain Point

  • Current in-car experiences are one-size-fits-all, failing to account for individual preferences or needs.
  • Manual adjustments while driving can be distracting and unsafe.
  • Accessibility gaps (e.g., for elderly passengers or those with hearing/visual impairments) remain unaddressed.

Challenge

Build a Generative AI-powered cockpit agent that dynamically personalizes the in-car experience based on contextual data such as:

  • Driver profile (age, preferences, past behaviour).
  • Calendar & journey type (work commute, leisure trip, urgent travel).
  • Mood (estimated from inputs like speech, facial cues, or self-reporting).
  • Accessibility needs (visual/hearing impairments, elderly passengers).

Goal

Deliver real-time, adaptive personalization of:

  • Comfort settings: AC, seat adjustments, lighting.
  • Infotainment: music, podcasts, news.
  • Navigation guidance: route optimization based on urgency, preferences, and accessibility.

Outputs

  • Dynamic in-car assistant that responds to context in real-time.
  • Personalized environment settings for comfort and safety.
  • Adaptive infotainment & navigation suggestions tailored to mood, journey type, and accessibility.

Impact

  • Safer driving experience with fewer distractions.
  • Higher passenger satisfaction through comfort and entertainment personalization.
  • Improved accessibility and inclusivity for diverse user needs.
  • New value proposition for automakers: cars as intelligent, personalized environments, not just vehicles.
AH2025/PS02 | AI/ML

Context

Automotive software development is highly complex, involving multiple tools (Jira, GitHub, MS Teams, Confluence), distributed teams, and strict compliance standards (ISO 26262, ASPICE). Project managers must continuously monitor tasks, track resources, and identify risks. However, the sheer volume of data across tools makes real-time visibility and decision-making difficult.

Pain Point

  • Project managers waste time manually consolidating data from Jira, GitHub, and communication platforms.
  • Resource allocation bottlenecks (overloaded developers, idle testers) often go unnoticed.
  • Risks (delays, defects, dependency issues) are only discovered late, impacting delivery timelines.
  • Lack of predictive insights leads to reactive, rather than proactive, project management.

Challenge

Build an AI-powered project management assistant that can:

  • Auto-generate project dashboards by integrating Jira, GitHub, and MS Teams data.
  • Provide real-time resource allocation insights (who is overloaded, who is free).
  • Predict risks and delays using historical patterns and live progress signals.
  • Deliver natural language summaries for managers and stakeholders.

Goal

Enable project managers to see the full picture instantly, automate reporting, and take data-driven decisions on resources and risks without manual effort.

Outputs

  • Automated project dashboards (progress, backlog, velocity, open PRs/issues).
  • Resource allocation map showing workload distribution across the team.
  • Risk prediction engine (e.g., “Module X likely delayed by 2 weeks due to dependency on Y”).
  • AI-generated summaries (daily/weekly status reports in plain language).

Impact

  • Reduced management overhead → fewer hours wasted on reporting.
  • Improved predictability → early identification of risks and delays.
  • Optimal resource utilization → balanced workloads across teams.
  • Better stakeholder communication → clear, automated updates.
  • Scalable for enterprises → can be deployed across multiple automotive software teams.
AH2025/PS01 | AI/ML

Context

In modern organizations, assembling the right project team is critical to success. Managers must balance skills, experience, cost, availability, and domain expertise, but decisions are often made using intuition or partial information. This leads to suboptimal teams, missed deadlines, or budget overruns.

Pain Point

  • Team formation today is time-consuming and heavily manual, requiring managers to cross-check spreadsheets, HR databases, and project needs.
  • Costs and expertise trade-offs are rarely quantified, making it hard to justify team composition to leadership or clients.
  • Traditional staffing tools focus on availability but fail to optimize across multi-dimensional constraints (skills, budget, past project fit, timeline).

Challenge

Build a Generative AI assistant that takes as input:

  • Employee database (skills, past projects, availability, cost)
  • Customer project requirements (tech stack, timeline, budget, domain)

Goal

Enable managers to form the best-fit, economically feasible project teams in minutes, rather than days, while providing transparency into why each recommendation was made.

Outputs

  • Optimal team composition: Recommended employees, with justification.
  • Economic feasibility analysis: Skill coverage vs cost vs timeline.
  • Alternative team recommendations: Trade-off scenarios (e.g., lower cost, faster delivery, more experienced).

Impact

  • Faster project staffing → quicker project kick-offs.
  • Higher client satisfaction due to right skills on the right project.
  • Lower staffing costs through data-driven optimization.
  • A scalable framework that can be extended for hackathons, consulting firms, or large enterprise project staffing.
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