Acsia Academy

Driving excellence, shaping the future, and fostering meaningful connections.

The Acsia Academy is focused on undertaking skill evaluation followed by upskilling and reskilling programs to ensure Acsians are competent and project-ready at all times.
The Acsia Academy works closely with the Advanced Technology Group and the Resource Management Group to run bespoke capability enhancement programs meeting specific project requirements.

The Agile Upskilling program empowers Acsians to embrace change and thrive in the fast-paced automotive industry. Carefully curated workshops and training sessions cover the latest advancements in technology, industry trends, and best practices, ensuring Acsians are always equipped with the skills to excel in their roles.

Competency Centers are at the heart of Acsia’s commitment to excellence. These specialized hubs of knowledge and expertise are dedicated to advancing Acsia’s specific domains within the automotive industry. Whether it is connected vehicles, autonomous driving, or advanced infotainment systems, our Competency Centers ensure that Acsians are equipped to deliver top-notch solutions to our customers.

Acsia’s Industry Connect program is a testament to the organization’s commitment to working together for a better future. Acsians believe in the power of partnerships and actively seek collaborations with other industry players, research institutions, and thought leaders. Through this program, Acsia drives innovation, shares knowledge, and collectively pushes the boundaries of what’s possible in automotive technology.

Explore Job Opportunities

Experience – 4 yrs to 11 yrs
Location – Bengaluru / Thiruvananthapuram

Job Description

Key Skills

C, QNX, Linux, Kernel, Device Drivers, MACSec, BSP, SoC Cybersecurity, Secure Boot, Cryptography, OPTEE, Penetration testing.

Required Skills

  • Technical Security concept and Software Security concept.
  • Vulnerability analysis (System and Software).
  • Threat analysis and risk assessment, Threat modelling.
  • Security Testing like Fuzz and Penetration testing.
  • Expertise in MACSec concepts.
  • Good understanding of HSM, Secure boot, Secure updates, cryptographic libraries, True Random number generator, Signing (ECDSA, RSA).
  • Good understanding on OPTEE OS, ARM Trusted Firmware, E-fusing.
  • Aware of Crypto terminologies like encryption (AES, ECC), signing (ECDSA, RSA), Hash (SHA-256).
  • Understanding on RFS protection like dm-verity.
  • Ability to work in a fast-paced environment building hardware and software products.
  • Good knowledge on Yocto framework is added advantage.

Preferred Skills

  • Experience in Automotive domain.
  • Experience in Linux build systems: Yocto.
  • Real-time systems programming experience considered an asset.
  • Experience with developing safety ISO26262 certified BSP and product considered an asset.
  • Experience in design tools such as EA and Rhapsody.
  • Contributions to Linux kernel and other open-source projects.
Apply

Experience – 4 yrs to 12 yrs
Location – Bengaluru

Job Description

  • Strong in C and C++ (C++ 11 is enough).
  • Understands Linux system programming, multi-threading,  POSIX methods.
  • Understands process scheduling.
  • Understands Linux audio stack including ALSA and PulseAudio (nice to have).
Apply

Location – Bengaluru
Experience – 3 to 6 yrs
Notice Period – 20 days max

Experience

  • Hands-on experience with Linux Kernel, device drivers, and embedded Linux systems.
  • Strong understanding of Linux internals, real-time Linux (RT), and Android Linux Kernel.
  • Experience with Linux driver porting, including UFS, file systems, I2C, SPI, Ethernet, UART, and display interfaces.
  • Proven ability to analyse and resolve kernel crashes.
  • Experience contributing to or working with open-source Linux development.
  • Experience in the Infotainment domain is a plus.
Apply

Location – Bengaluru
Experience – 6 to 8 yrs
Notice Period – 20 days max

Job Description:

  • Strong experience in C/C++ development.
  • Proficiency in Python is highly desirable.
Apply

Location – Thiruvananthapuram
Experience – 5 to 9 yrs

Experience

  • 5 to 9 years of experience in testing within the automotive domain.
  • In-depth knowledge of CAN, UDS, and Automotive Ethernet protocols.
  • Hands-on expertise in SIL (Software-in-the-Loop) and HIL (Hardware-in-the-Loop) system validation.
  • Proficiency in test automation with flexibility to work in Python, C, and C++.
  • Experience in Linux and Android platform testing.
  • Strong grasp of core testing concepts, strategies, and best practices.
Apply

Location – Thiruvananthapuram

Experience – 5 to 9 yrs

  • 3-5 years of hands-on experience in developing and implementing Generative AI solutions.
  • Hands-on experience with Agentic AI frameworks and multi-agent systems, including:
    • Deep familiarity with Large Language Models (LLMs) such as GPT, LLaMA, Claude, MISTRAL, QWEN.
    • Experience with AI frameworks and libraries including PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex or similar.
    • Strong experience in implementing Retrieval-Augmented Generation (RAG) frameworks and techniques.
  • Proficiency in programming languages used in AI development (e.g., Python).
  • Experience with Virtual Private Cloud platforms (AWS, Azure, Google Cloud) and deploying AI solutions in cloud environments.
  • Solid understanding of machine learning fundamentals and natural language processing concepts.
  • Excellent problem-solving skills, with the ability to creatively approach AI challenges and optimize AI model performance.
  • Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders.
Apply

Location – Thiruvananthapuram

Experience – 5 to 9 yrs

  • 4–5 years of hands-on experience in data science projects, preferably across different domains.
  • Strong statistical expertise, including hypothesis testing, linear and non-linear regression, classification techniques, and probability distributions.
  • Proven ability to translate complex business problems into data science use cases with practical impact.
  • Experienced in building and validating machine learning models, including classification, regression, and survival analysis.
  • Proficiency in Python, including libraries like Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn, as well as SQL for data querying and analysis.
  • Experience handling both structured and unstructured datasets, with expertise in exploratory data analysis (EDA) and data cleaning.
  • Strong communication skills, with the ability to explain technical concepts clearly to non-technical stakeholders.
  • Familiarity with version control tools such as Git/GitHub and collaborative development workflows.
  • A deployment mindset, with an understanding of how to build data products that are usable, scalable, and maintainable.
Apply

Location – Thiruvananthapuram

Experience – 4.5 to 9 yrs

Notice Period – 30 days max

Job Description 

  • Proven expertise in C++ middleware development for Android/Linux-based IVI and cockpit systems, including end-to-end HAL/VHAL implementation and customization.
  • In-depth experience with Android AOSP, HIDL, frameworks, and system-level components such as multimedia, Bluetooth, projection, and diagnostics.
  • Strong understanding of automotive IPC and IPCL protocols, with hands-on proficiency in cross-compilation and build systems like CMake, Soong, and Gradle.
  • Solid foundation in object-oriented design and design patterns, with strict adherence to industry coding standards such as MISRA C/C++.
  • Practical experience working with Android/Linux/QNX toolchains, advanced debugging tools like Android Debug Bridge (ADB), and collaborative development using Git version control.
Apply

Location – Thiruvananthapuram

Experience – 6 to 10 yrs

Notice Period – 90 days max

What We’re Looking For

  • Hands-on expertise in modern development environments.
  • Passion for automotive technology and software excellence.

Tech Stack

  • C++17/20
  • Yocto Linux
  • CMake
  • Git/GitHub EE
  • Zuul CI
Apply

Candidate profile

  • Any graduate available to work full-time from Acsia Global Headquarters, Technopark Phase 3, Thiruvananthapuram.
  • English speaking and writing skills.
  • Smart, self-motivated and closure-driven.

Scope of work

  • Use LinkedIn and business intelligence tools like Apollo and Kaspr to gather contact details (name, designation, email, mobile) of key decision makers from companies in the automotive industry. 
  • Gather the information and populate MS Excel template provided.

Desirable but not compulsory

  • Past experience in a similar role. 
  • MS Excel proficiency.
Apply

Location – Thiruvananthapuram 
Experience – 5 to 9 yrs 

Job Description 

  • Proven expertise in AOSP framework development with 5+ years of experience; IVI domain exposure preferred. 
  • Strong understanding of Android HAL and AOSP architecture, including policies, configs, and board files. 
  • Skilled in analyzing and resolving CTS/VTS issues to ensure platform compliance and stability. 
  • Capable of independently debugging complex framework and system issues with effective root-cause analysis. 
  • Proficient in Java; C/C++ knowledge is an added advantage. 
Apply
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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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