AI Engineer

4 days ago

Salem, India TechKareer Full time

Role: AI EngineerExperience: 1–3 yearsLocation: Mumbai / Bengaluru / Gurgaon (Hybrid: 3 days/week in office)Remote option for exceptional candidates.About the RoleWe’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 DoDesign, 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 SkillsHands-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-HaveFastAPI/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 MetricsReduction 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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