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Job Description & How to Apply Below
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, Sage Maker, Vertex AI, or equivalent) on AWS / GCP / Azure.
● Strong debugging, profiling, and optimization skills across data pipelines, model performance, inference latency, and cost.
Full-Stack & System Awareness:
● 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 (Lang Chain, Llama Index, Lang Graph, 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, Tensor
RT-LLM.
● Experience with responsible AI: safety, guardrails, red-teaming, 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.
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