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GenAI/Applied AI

Interface Microsystems

Pune

3-5 years

Today

$26.5K–41.0K/yr

Full time

Onsite

Skills Required

LLM

RAG

Prompt Engineering

Anthropic

OpenAI

Fine-tuning

Agent Frameworks

Python

NLP

Deep Learning

Domain Adaptation

Knowledge Graphs n8n

Evaluation

Observability

Description

GenAI/Applied AI role focused on building internal AI workflows and prototypes. The position supports automotive engineering use cases and broader AI+X discovery work.

Role: GenAI/Applied AI

Location: Pune, India

Qualification

- Bachelor's in Computer Science, AI/ML, or related field
- Master's in Computer Science, AI/ML, or related field

Responsibilities

- Build and maintain RAG pipelines
- Build and maintain prompt and agent workflows
- Build and maintain evaluation harnesses for internal engineering use cases
- Support fine-tuning and domain adaptation of LLMs for automotive requirements engineering and compliance documentation
- Reduce hallucination and improve reliability using knowledge-graph grounding and auto-evaluation frameworks
- Prototype AI-assisted tools for requirements drafting, traceability, and test-case generation supporting the ASPICE pipeline
- Explore applied-AI use cases beyond engineering under the AI+X mandate
- Document and present findings and working prototypes to engineering and product stakeholders
- Check evaluation dashboards and overnight pipeline runs
- Triage failures and hallucination or quality regressions
- Iterate on RAG pipelines, prompts, and agent workflows for the ASPICE SOW-to-artifact use case
- Pair with a Systems Software Engineer to test generated requirements or test cases against real project data and capture feedback




- Prepare short demos for stakeholders
- Review use-case prioritisation with the GenAI/Applied AI Lead
- Participate in periodic AI+X discovery sessions with SW, HW, BD, and HR

Additional Responsibilities

- Prepare datasets for fine-tuning and domain-adaptation experiments
- Extend the evaluation harness by adding cases, measuring accuracy and hallucination rate, and logging results
- Update the prototype backlog

Nice To Have

- Exposure to automotive or engineering-domain data
- Experience with workflow-automation frameworks such as n8n
- Familiarity with evaluation and observability practices for LLM systems
- Awareness of ASPICE terminology
- Analytical mindset
- Comfort with ambiguity typical of a proving-ground role

More Skills LLM APIs, knowledge-graph grounding, evaluation harnesses, auto-evaluation frameworks, workflow-automation frameworks, LLM observability, hallucination reduction, ASPICE

Prepare for this role

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