AI LEAD

We are seeking an experienced AI Lead to own the design, delivery, and scaling of production AI systems powered by Generative AI, Agentic AI, and Retrieval-Augmented Generation. You will lead cross-functional engineering teams through agile sprints, drive enterprise AI adoption, and build intelligent systems that autonomously reason, retrieve, and act across complex workflows.

•      Lead end-to-end delivery of AI/ML projects using Agile/Scrum—sprint planning, backlog management, retrospectives, and on-time releases across multiple workstreams.

•      Architect and deploy production GenAI applications: AI copilots, chatbots, content generators, summarisation engines, and code assistants using GPT, Claude, Gemini, LLaMA, and Mistral.

•      Design and scale enterprise RAG pipelines—document ingestion, semantic chunking, embedding generation, vector search (Pinecone, Weaviate, ChromaDB), hybrid retrieval with BM25 + re-ranking, and advanced patterns like GraphRAG, corrective RAG, and agentic RAG.

•      Build and orchestrate autonomous Agentic AI systems with multi-agent collaboration, ReAct reasoning loops, plan-and-execute architectures, persistent memory, tool-use/function-calling, and human-in-the-loop checkpoints using LangChain, LangGraph, CrewAI, or AutoGen.

•      Design integration architectures connecting AI services to enterprise platforms (Salesforce, SAP, ServiceNow, Slack) via REST, GraphQL, gRPC, Kafka, MCP servers, and webhook-based event-driven triggers.

•      Drive LLM fine-tuning using LoRA, QLoRA, PEFT, instruction tuning, and RLHF/DPO for domain-specific model adaptation; implement guardrails, hallucination detection, and output validation.

•      Own MLOps infrastructure—CI/CT/CD pipelines, model monitoring, drift detection, A/B testing, automated retraining, and containerised serving on Docker/Kubernetes across AWS, Azure, or GCP.

•      Establish agent observability and tracing (LangSmith, Arize Phoenix) and RAG evaluation frameworks (RAGAS) to ensure reliability, performance, and cost-efficiency at scale.

•      Manage GPU/TPU compute, cost optimisation, auto-scaling, and multi-region high-availability deployments for mission-critical AI services.

•      Design and deploy time series forecasting models for demand planning, anomaly detection, predictive maintenance, and financial forecasting using statistical methods (ARIMA, Prophet), deep learning (LSTM, Temporal Fusion Transformers, N-BEATS), and foundation time series models.

•      Build end-to-end ML pipelines for classical AI/ML use cases—classification, regression, clustering, recommendation systems, and computer vision—ensuring robust feature engineering, model selection, and hyperparameter optimisation at scale.

•      8–12 years in AI/ML engineering with 3+ years in a technical lead or architect role.

•      Bachelor’s/Master’s in Computer Science, ML, AI, Data Science, or a related quantitative field.

•      Deep hands-on expertise in Python, TensorFlow, PyTorch, Hugging Face Transformers, and scikit-learn.

•      Proven production experience with GenAI applications, LLM fine-tuning, RAG systems (vector DBs, embedding pipelines, hybrid search), and Agentic AI (multi-agent orchestration, tool use, memory).

•      Strong experience integrating AI systems with enterprise platforms via APIs, event-driven architectures, and middleware.

•      Proficient in cloud platforms (AWS SageMaker/Bedrock, Azure ML, GCP Vertex AI) and IaC (Terraform, CloudFormation).

•      Track record of running agile sprints and delivering complex AI projects on schedule.

•      Hands-on experience building time series forecasting and anomaly detection models using statistical and deep learning approaches (ARIMA, Prophet, LSTM, Transformer-based models).

•      Ph.D. in ML, NLP, Computer Vision, or Reinforcement Learning.

•      Experience with knowledge graphs (Neo4j), GraphRAG, edge AI deployment, and model optimisation (quantisation, distillation).

•      Contributions to AI research (publications, patents, open-source) and certifications in cloud AI/ML services.

•      Familiarity with responsible AI—guardrails, bias detection, content moderation, and explainability (SHAP, LIME).

GenAI & LLMs	GPT, Claude, Gemini, LLaMA, Mistral, Cohere; LoRA, QLoRA, RLHF, PEFT
Agentic AI	LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, LangSmith
RAG & Search	Pinecone, Weaviate, Milvus, ChromaDB, Qdrant, pgvector, FAISS, Cohere Rerank
ML Frameworks	TensorFlow, PyTorch, Keras, Hugging Face, scikit-learn, JAX, Prophet, statsmodels
Integrations	REST, GraphQL, gRPC, Kafka, WebSockets, MCP Servers, OAuth 2.0
MLOps & Infra	MLflow, Kubeflow, Airflow, Docker, Kubernetes, Terraform, GitHub Actions
Cloud	AWS (SageMaker, Bedrock), Azure (Azure ML, OpenAI Service), GCP (Vertex AI)

•      Competitive salary with performance bonuses and equity; access to cutting-edge GPU infrastructure.

•      Generous L&D budget for conferences (NeurIPS, ICML), certifications, and advanced training.

•      Flexible hybrid work, comprehensive benefits, and the opportunity to shape company-wide AI strategy.