Senior AI/ML Engineer

We are seeking a highly skilled Senior AI/ML Engineer to drive AI features from concept to production. This role is pivotal in managing the entire lifecycle of our LLM-powered systems. You will be responsible for benchmarking model and pipeline performance, scaling the technology stack, and deploying live solutions for our users.

This position bridges applied LLM/GenAI work with MLOps, critically influencing our ability to deliver new AI capabilities to customers efficiently and reliably. You will collaborate closely with product, backend, and design teams to ensure our shipped products are fast, accurate, cost-effective, and observable in production.

What you will do

• Design, build, and deploy end-to-end LLM-powered features, including RAG pipelines, agentic workflows, prompt orchestration, and targeted fine-tuning.
• Establish and execute rigorous benchmarking frameworks for AI applications, evaluating latency, throughput, accuracy, hallucination rates, cost per request, and quality regressions.
• Implement both offline (golden sets, LLM-as-judge, human-in-the-loop) and online (A/B tests, shadow traffic, canary releases) evaluation strategies prior to production deployment.
• Transition models and pipelines to production, encompassing containerization, deployment, autoscaling, and inference service monitoring with defined Service Level Objectives (SLOs) for latency, error rate, and cost.
• Develop the MLOps infrastructure, including CI/CD for models and prompts, versioning, feature stores, comprehensive observability (traces, metrics, logs), and robust rollback mechanisms.
• Optimize inference performance and cost through techniques such as batching, caching, quantization, distillation, model routing, and strategic decisions between managed and self-hosted solutions.
• Partner with product management to translate product requirements into measurable AI quality standards and make data-driven decisions regarding production readiness.
• Mentor junior engineers on LLM best practices, evaluation methodologies, and production deployment standards.

What we are looking for

• 3-6 years of engineering experience, with significant hands-on experience shipping ML or AI systems to production environments, beyond notebooks or proof-of-concepts.
• Proven expertise in LLMs and GenAI, including experience with production systems using OpenAI, Anthropic, or open-source models, and practical knowledge of RAG, embeddings, vector stores, and prompt engineering.
• A strong MLOps foundation, including experience with model serving (e.g., FastAPI, vLLM, Triton, SageMaker), containerization (Docker, Kubernetes), and at least one major cloud platform (AWS, GCP, or Azure).
• Demonstrated capability in rigorous system benchmarking, with the ability to articulate how model quality and performance were measured, optimized, and what trade-offs were made.
• Proficiency in Python, with comfort in PyTorch or TensorFlow, and familiarity with frameworks like LangChain, LlamaIndex, or equivalent.
• Excellent engineering discipline, including testing, code reviews, clear API design, and proactive implementation of observability.
• Comfort and experience in owning the entire path to production, including troubleshooting and resolving issues independently.

Bonus points

• Experience with fine-tuning or post-training open-source models (e.g., LoRA/QLoRA, DPO, RLHF).
• Familiarity with multimodal models (image, video, or audio generation/understanding).
• Experience building or contributing to internal evaluation harnesses or LLM observability tooling.
• Proven experience with high-QPS, low-latency inference at consumer scale.
• Open-source contributions or technical writing in the AI/ML domain.

What success looks like in your first 6 months

• Successfully shipped at least one customer-facing AI feature to production, with full ownership.
• Established and operationalized a benchmarking and evaluation framework for all model and prompt changes, with visible results for the team.
• Implemented clear SLOs, dashboards, and alerting for production AI services, with precise cost-per-request metrics readily available.
• Measurably increased the team's velocity in shipping AI features through implemented infrastructure and best practices.

Location: Chennai, Tamil Nadu