Ai engineer

2 days ago

Kakinada, India TechKareer Full time

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/Fast API) 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 (Open AI, Claude, Llama, etc.) and orchestration frameworks (Lang Chain, Llama Index, 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 Fast API/Flask, Celery/queues; streaming UIs. Model fine-tuning/Lo RA, dataset curation, prompt-cache strategies. Monitoring/tracing (Lang Smith, Weights & Biases, Open Telemetry). 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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