Job Description :

We are seeking an AI Solutions Lead to architect, govern, and grow our AI delivery practice across GenAI, Agentic AI, and applied ML engagements. This is a hands-on that includes architecting AI solutions and shaping the growth of the AI practice. This role is suited for someone who has earned technical fluency with GenAI and agentic AI on top of a strong foundation in classical ML and deep learning and who is now ready to set the technical direction for a growing team.

Required Technical Skills :

- Programming & Engineering : Python (advanced), SQL; strong API and backend engineering in FastAPI/Flask/Django; production-grade software practices.

- Generative AI : LLMs and SLMs, RAG/Agentic RAG, multimodal architectures, agents, prompt engineering, grounding, knowledge graphs, fine-tuning (SFT, LoRA/QLoRA, RLHF/RLAIF), distillation, and quantization.

- Agentic AI : Multi-agent orchestration, planning, tool use, persistent memory, MCP and A2A patterns; frameworks such as LangGraph, LlamaIndex, AutoGen.

- Eval & Safety : Eval framework design, golden datasets, automated and human evals, red-teaming, guardrails, hallucination control, observability for AI systems.

- Machine Learning & Deep Learning : Predictive modeling, deep learning (CNNs, RNNs/LSTMs, Transformers), embeddings, vector search, classical ML; CV, NLP, and time-series exposure.

- Cloud, MLOps & Deployment : AWS, Azure, or GCP at depth; model serving, GPU/accelerator ops, CI/CD, monitoring, on-prem and edge deployment patterns.

- Data Engineering : Kafka, Spark/Flink, Hadoop, MongoDB and other NoSQL/graph/vector stores; large-scale streaming and batch pipelines.

- Math Foundations : Linear algebra, probability, statistics, optimization.

- Experience with commerce cloud ecosystems (good to have) Salesforce and Adobe

Experience Requirements :

- 10-12 years of hands-on experience building and deploying ML, DL, and AI systems in production, with progression into solution architecture and technical leadership

- 10+ years of demonstrable experience working with global businesses, delivering on large accounts

- 3+ years of demonstrable hands-on work in GenAI and/or Agentic AI - beyond prompt engineering and basic RAG - including multi-agent systems, custom fine-tuning, multimodal pipelines, or SLM-based deployments.

- Proven track record of architecting and shipping AI systems in enterprise-grade environments, including regulated or high-stakes domains.

- 3+ Experience leading ML-AI technical pods or teams (formal or dotted-line), mentoring senior engineers, and setting hiring and review standards.