3–6 yrsChennaiOn-siteDirect career pageEst. 18–35 LPA
Posted on 10 Sept
Reach people at Zocket (Zocket Technologies Private Limited)
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Zocket (Zocket Technologies Private Limited) you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Own end-to-end development and production deployment of LLM-powered AI features including RAG pipelines, agentic workflows, and fine-tuning where applicable.
- Define and implement benchmarking and evaluation frameworks (offline and online) to measure model quality, performance, cost, and ship readiness.
- Build and maintain MLOps infrastructure including model serving, CI/CD, observability, autoscaling, and cost-optimization strategies for production AI systems.
Minimum Requirements
- 3–6 years of engineering experience with demonstrated production ML/AI system shipping (beyond prototypes or notebooks).
- Hands-on experience with LLMs and GenAI including production use of OpenAI, Anthropic, or open-source models, plus practical knowledge of RAG, embeddings, vector stores, and prompt engineering.
- Strong MLOps skills including model serving (FastAPI, Triton, SageMaker, or similar), containerization (Docker, Kubernetes), and cloud experience (AWS, GCP, or Azure).
- Proficient in Python and familiar with PyTorch or TensorFlow and AI frameworks like LangChain or LlamaIndex.
Ideal Candidate Profile
- Experienced in taking complex AI systems from prototype to production independently with strong ownership over live deployments and incident resolution.
- Skilled at rigorously benchmarking AI models for multiple KPIs and using data-driven criteria to decide feature readiness for shipping.
- Engineering-focused mindset emphasizing testing, observability, API design, and cost-performance optimization within production AI services.