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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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