AI Engineer

15 hours ago

Mumbai Metropolitan Region, India TechKareer Full time ₹ 30,00,000 - ₹ 31,50,000 per year

AI Engineer (LLMs, Agentic Apps)
Experience:
1–3 years

Compensation:
₹25-30 LPA

Openings:
7–8

Location:
Mumbai / Bengaluru / Gurgaon (Hybrid: 3 days/week in office). Remote option for exceptional candidates.

About The Role
We're building production-grade AI workflows and agentic applications that power real user experiences. As an AI Engineer, you'll ship features end-to-end—from prompt design and evaluation to scalable backend integration—working closely with product and engineering.

What You'll Do
Design, build, and iterate LLM-powered workflows (retrieval, routing, tool use, function calling, multi-step agents).

Implement agentic apps that plan, call tools/APIs, and maintain state across tasks.

Build RAG pipelines: data ingestion, chunking, embeddings, indexing, and latency-optimized retrieval.

Own prompt engineering & evaluation (A/B tests, guardrails, metrics like latency, cost, quality, safety).

Productionize models/workflows with observability (traces, tokens, failures), cost controls, and fallbacks.

Ship backend services and APIs (e.g., Python/FastAPI) integrating with data stores and vector DBs.

Collaborate with PM/Design to translate requirements into reliable, user-facing features.

Must-Have Skills
Hands-on with LLMs (OpenAI, Claude, Llama, etc.) and orchestration frameworks (LangChain, LlamaIndex, or custom).

Python proficiency; building RESTful services, writing clean, tested code.

Experience with RAG, vector databases (Pinecone, Weaviate, FAISS, Qdrant), embeddings.

Understanding of agent patterns (tool calling, planning/execution, memory) and workflow engines.

Familiarity with prompt design, safety/guardrails, and evaluation frameworks.

Basics of cloud & deployment (AWS/GCP/Azure), Docker, Git, CI/CD.

Strong debugging mindset and bias to ship.

Nice-to-Have
FastAPI/Flask, Celery/queues; streaming UIs.

Model fine-tuning/LoRA, dataset curation, prompt-cache strategies.

Monitoring/tracing (LangSmith, Weights & Biases, OpenTelemetry).

Frontend basics ) to collaborate on UX.

Data privacy, PII redaction, and security best practices.

Success Metrics
Reduction in latency/cost per request; improvement in answer quality scores.

Workflow reliability (timeouts, retries, fallbacks) and on-call readiness.

Speed of iteration from spec → prototype → production.

Skills: machine learning,backend development,python

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