Talent.com
AI Engineer

AI Engineer

TechKareerGulbarga, IN
30+ days ago
Job description

Role : AI Engineer

Experience : 1–3 years

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 (React / Next.js) 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.
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    Ai Engineer • Gulbarga, IN