India Wire harness engineer Jobs – 95 Open Jobs Today

Senior AI / RAG Engineer — Applied LLM & Retrieval
TechGrove by Banyan Software —Bengaluru, Karnataka
- Email
- Remote
- Master's degree
- AWS
- APIs
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3h
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Janatics —Malumichampatti, Coimbatore, Tamil Nadu
- Email
- Laboratory experience
- Master's degree
- Analysis skills
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Hyland Software —Remote
- Remote
- Travel
- Agile
- AI
Jr. Design Engineer (Wire Harness)
Access-CADD —India
- Email
- Schematics
- Bachelor's degree
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Kantiv (formerly Joist AI) —India
- Full-time
- Remote
- Master's degree
- Plumbing
- AI
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Hyland —India
- Full-time
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Senior AI / RAG Engineer — Applied LLM & Retrieval

TechGrove by Banyan Software
Bengaluru, Karnataka

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Job details

3 hours ago

Qualifications

CI/CD
Law
Multilingual
Test automation
Master's degree
AWS
Continuous integration
APIs
JSON
AI
Python
Identity & access management
SDLC

Full job description

TechGrove is the Centre of Excellence for Banyan Software, based in Chennai, India. It plays a key role in supporting Banyan's global businesses through technology, security, and software development. TechGrove brings together India's deep pool of technical talent with Banyan's long-term approach to growth, creating a trusted, developer-focused environment where people can do their best work.

Senior AI / RAG Engineer — Applied LLM & Retrieval

This is a deeply hands-on role for an experienced engineer who wants to spend their time building. A big part of the job is growing our fleet of AI sub-agents — designing new specialized agents and wiring them into complete, end-to-end multi-agent agentic workflows and architecture. You'll design, implement, and harden our RAG, agent, and LLM features end-to-end — the retrieval pipelines, the agent orchestration, the model-gateway routing and fallback logic, the AI automation pipelines, and the evaluations that keep quality high — all inside a GDPR-first, EU-only data boundary. Your work ships to production and you own it through to it working reliably.

What you'll do

Grow our fleet of AI sub-agents — design and build more and more specialized agents, and wire each into a complete multi-agent, agentic AI-driven workflow and architecture, from single-purpose agents up to orchestrated end-to-end flows.
Build and improve RAG pipelines — chunking, embeddings, vector storage and retrieval, re-ranking, grounding and citation — over proprietary data such as work orders, rate cards, and uploaded documents.
Build LLM features on cloud-hosted large language models — classification, extraction, summarisation, structured/JSON output, prioritisation, and multimodal (text + image) reasoning — from prototype through to production.
Extend the internal Model Gateway — task/tier model routing, retries, fallback, cost estimation, and per-tenant usage limits.
Build adversarial validation agents — AI agents that critique and stress-test model outputs, plans, and designs over multiple rounds to catch errors and edge cases before they ship.
Design and build AI automation pipelines — orchestrated, repeatable AI workflows, both in the product and across our engineering process.
Automate the SDLC with AI — bring AI into how we plan, generate, review, test, and ship code, and build the tooling that makes it repeatable.
Integrate and extend an automated testing framework — including AI-assisted test generation and self-checking pipelines for our AI features.
Build the evaluations — datasets and harnesses that measure accuracy, faithfulness/hallucination, latency, and cost, and catch regressions in non-deterministic systems before release.
Do the prompt engineering — schema-validated outputs, tool/function calling, and the tuning needed to reduce hallucination and hit quality targets.
Enforce data-protection by design — tenant-scoped retrieval, PII handling, and erasure/cascade in the retrieval layer; keep every data flow inside the EU/EEA.
Ship to production on AWS (Python, ECS) with proper observability, and own your features through to reliable operation.

Required skills & experience

All of the following are required.

5–8+ years of software engineering, with deep, production-grade Python.
Substantial hands-on experience building and shipping LLM-powered systems to production — you've taken more than one from idea to reliable, scaled feature and stayed close to the code.
Designing and composing multi-agent systems — building specialized sub-agents and wiring them into complete, end-to-end agentic workflows and architecture.
Deep RAG expertise — embeddings, vector stores (e.g. pgvector, OpenSearch, Pinecone, FAISS), semantic search, chunking strategies, re-ranking, grounding — and the judgement to know what actually moves quality.
Embeddings at scale and multilingual retrieval.
Advanced prompt engineering and structured output / tool use / function calling with schema-validated (JSON) responses; strong instincts for reducing hallucination.
Deep experience with cloud-hosted commercial LLM APIs / a managed LLM platform.
Agentic / multi-agent frameworks (LangGraph, LangChain, or similar) — orchestration and state machines — including building adversarial / validation agents or LLM-as-judge setups.
AI-driven SDLC automation and experience building AI automation pipelines (CI/CD or workflow orchestration).
Integrating automated testing frameworks and AI-assisted test automation.
Proven ability to build evaluations for AI systems — datasets, metrics (accuracy, faithfulness, latency, cost), and regression testing.
Real command of cost/latency trade-offs — model tiering, routing, fallback, and caching — at production scale.
Strong AWS background — building, deploying, and operating services in production; IAM and region-aware design.
Streaming, latency optimisation, and prompt caching.
Vector-DB operations, observability (Datadog / CloudWatch), and IaC.
GDPR / data-residency / responsible-AI experience — no-training commitments, tenant isolation, DPAs, PII minimisation.
Experience with AI systems running at production scale.
Excellent testing discipline, code quality, and engineering judgement; comfortable owning a feature end-to-end.

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wire harness engineer jobs in india

Senior AI / RAG Engineer — Applied LLM & Retrieval

Senior Design Engineer - Electrical

Senior Product UX Designer (Automation and AI)

Jr. Design Engineer (Wire Harness)

Agentic Systems Engineer

Senior Product UX Designer (Automation and AI)

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