Staff Engineer - GenAI

AjnaLens; Thane; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Ajna Lens is seeking an Associate Staff / Staff AI Engineer to join our AI engineering team in Thane, Maharashtra. This senior IC role focuses on turning AI research into production-grade products, blending applied ML, large language models, GenAI systems, and scalable AI infrastructure.

You will own major parts of the AI stack, design GenAI apps, and drive cross-functional collaboration with Product, Data, Platform, Backend, and Research teams to deliver measurable business impact.

Qualifications

• 8–12 years in AI/ML engineering across applied ML, deep learning, NLP/CV, or GenAI systems (10+ years for Staff level).
• Hands‑on experience shipping AI products from research/prototype to production deployment with real users.
• Strong exposure to cross‑functional collaboration with Product, Data, Platform, Backend, and Research teams.

Responsibilities

• Own and architect the end-to-end AI stack — from data and models to evaluation, serving, and monitoring — for production AI features.
• Design and build GenAI systems including LLM applications, RAG pipelines, fine-tuned models, and agentic workflows that deliver measurable business impact.
• Drive system optimization across model quality, latency, cost, reliability, and safety to make AI experiences production-ready at scale.

Skills

Python

Software engineering

PyTorch

Transformers

LLM development

RAG pipelines

Fine-tuning

Docker

Kubernetes

ML ops

SageMaker

Vertex AI

LangChain

LlamaIndex

FAISS

Pinecone

Milvus

System design

Education

B.E. / B.Tech / M.E. / M.Tech / M.S. / Ph.D. in Computer Science, AI/ML, Data Science, Electronics, Mathematics, or related fields

Tools

Docker

Kubernetes

MLflow

Kubeflow

SageMaker

Vertex AI

OpenAI API

Anthropic API

Google API

vLLM

Job description

Ajna Lens is looking for an Associate Staff / Staff AI Engineer to join our AI engineering team in Thane (Maharashtra – India). This is a high-impact senior IC role for an experienced engineer who will help build next-generation AI products by combining applied machine learning, large language models, GenAI systems, and production-grade AI infrastructure. The ideal candidate should have 8–12 years of experience in AI/ML engineering with strong expertise in deep learning, LLM-based systems, RAG and agentic architectures, model deployment, and large-scale AI system design. This role demands strong technical ownership, cross-functional leadership, and the ability to convert AI research and prototypes into production-grade products used by real users.

Top 3 Daily Responsibilities:

• Own and architect the end-to-end AI stack — from data and models to evaluation, serving, and monitoring — for production AI features.
• Design and build GenAI systems including LLM applications, RAG pipelines, fine-tuned models, and agentic workflows that deliver measurable business impact.
• Drive system optimization across model quality, latency, cost, reliability, and safety to make AI experiences production-ready at scale.

Minimum Work Experience Required:

• 8–12 years in AI/ML engineering across applied ML, deep learning, NLP/CV, or GenAI systems (10+ years for Staff level).
• Hands‑on experience shipping AI products from research/prototype to production deployment with real users.
• Strong exposure to cross‑functional collaboration with Product, Data, Platform, Backend, and Research teams.

Top 5 Skills You Should Possess:

• Strong programming expertise in Python, with solid software engineering fundamentals (testing, design patterns, performance).
• Deep hands‑on experience with modern ML frameworks — PyTorch, Hugging Face Transformers, scikit‑learn — and the full model lifecycle (training, fine‑tuning, evaluation, deployment).
• Strong GenAI expertise: LLM application development, prompt engineering, RAG, embeddings, vector databases (FAISS, pgvector, Pinecone, Milvus), fine‑tuning (LoRA/PEFT/SFT), and evals.
• Experience deploying AI models in production using Docker, Kubernetes, and MLOps tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or equivalent) on AWS / GCP / Azure.
• Strong debugging, profiling, and optimization skills across data pipelines, model performance, inference latency, and cost.
• Strong understanding of AI system architecture — data layer, feature stores, training infra, model registry, serving, and observability.
• Experience integrating retrieval systems, vector stores, caching layers, streaming pipelines, and orchestration frameworks (LangChain, LlamaIndex, LangGraph, or in‑house equivalents).
• Familiarity with LLM provider APIs (OpenAI, Anthropic, Google, open‑source models via vLLM/TGI), routing, fallbacks, and hybrid model strategies.
• Awareness of how AI features integrate with product surfaces — backend APIs, mobile apps, and web clients — including latency and UX trade‑offs.

Leadership & Strategic Capabilities:

• Ability to independently own large, ambiguous AI initiatives and deliver end‑to‑end with measurable outcomes.
• Strong problem‑solving mindset with a balance of research rigour and production engineering discipline.
• Ability to mentor engineers, drive design reviews, and raise the technical bar across the AI org.
• Strong documentation practices for design decisions, experiments, evaluation results, and post‑incident learnings.

Bonus Points For:

• Publications at top‑tier AI/ML venues (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR) or significant open‑source contributions.
• Experience building agentic systems, tool‑use, multi‑agent orchestration, or advanced LLM eval frameworks.
• Experience with inference optimization — quantization, distillation, speculative decoding, vLLM, TensorRT-LLM.
• Experience with responsible AI: safety, guardrails, red‑team​ing, bias evaluation, and policy compliance.
• Domain depth in a high‑scale area — search, recommendations, voice/speech, vision, or enterprise GenAI.

What You’ll Be Creating:

• Production‑grade GenAI products powered by LLMs, RAG, and agentic intelligence used by real customers.
• Highly optimized AI systems delivering low latency, high reliability, and predictable cost at scale.
• Robust ML platforms and pipelines covering training, evaluation, deployment, and continuous improvement.
• Advanced AI experiences combining language, vision, voice, and contextual reasoning.
• A scalable AI foundation that powers the next generation of intelligent products for millions of users.

Education:

• B.E. / B.Tech / M.E. / M.Tech / M.S. / Ph.D. in Computer Science, AI/ML, Data Science, Electronics, Mathematics, or related fields.
• Equivalent product experience with a strong AI delivery track record is highly valued.