Senior AI Engineer – GenAI, Agentic AI & Machine Learning

You are a driving force behind our next-generation AI capabilities. As Senior AI Engineer, you architect and deliver cutting-edge Generative AI, Agentic AI, and classical Machine Learning solutions – from rapid prototyping to production-grade systems. You bridge the gap between AI research and real-world industrial impact, working at the intersection of autonomous AI agents, large language models, and robust ML engineering. As an experienced engineer, you naturally guide and uplift junior team members along the way.

·        Design autonomous AI agent systems - multi‑agent orchestration, tool‑use frameworks, ReAct, Plan‑and‑Execute, LangGraph, CrewAI, AutoGen.

·        Architect production‑grade GenAI applications - RAG pipelines, fine‑tuning, prompt engineering, guardrails, evaluation.

·        Integrate LLMs - function calling, structured outputs, multimodal models, embeddings, vector DBs like Pinecone, Weaviate, Qdrant, pgvector.

·        Implement agentic workflows for autonomous reasoning, retrieval, decision‑making, and action.

·        Evaluate GenAI/agent solutions - hallucination detection, faithfulness metrics, latency, cost.

·        Develop ML models - classification, regression, time‑series, anomaly detection, NLP, computer visio.

·        Build end‑to‑end ML pipelines - feature engineering, training, hyperparameter optimization, validation, serving.

·        Apply hybrid GenAI + ML approaches and ensure data quality and feature store practices.

·        Design scalable MLOps pipelines, CI/CD workflows, model registries, automated retraining.

·        Deploy across on‑prem, cloud (AWS/Azure/GCP), edge, and air‑gapped environments

·        Implement observability - monitoring, drift detection, A/B testing, logging, alerting.

·        Use DevOps tools - Kubernetes, Docker, Terraform/Ansible, GitHub/GitLab CI.

·        Mentor junior engineers (code reviews, pair programming, best practices).

·        Translate business needs into AI solutions and contribute to AI strategy and roadmaps.

·        Production experience with GPT‑4/Claude/Gemini/Llama/Mistral; fine‑tuning, RLHF, quantization, prompt engineering.

·        Hands‑on with agent frameworks (LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel).

·        Strong RAG expertise (hybrid search, reranking, chunking, RAGAS, DeepEval).

·        Experience with vector databases (Pinecone, Weaviate, Qdrant, Milvus, pgvector).

·        Guardrails, safety, hallucination mitigation, and systematic evaluation.

·        Supervised/unsupervised learning, ensemble methods, deep learning (PyTorch/TensorFlow).

·        Industrial use cases (anomaly detection, predictive maintenance, time‑series, NLP, CV).

·        Strong experimentation and hybrid thinking.

·        5+ years IT, 3+ years AI/ML.

·        Python must‑have; Go/Java/Rust a plus.

·        MLOps tools - MLflow/W&B, TorchServe/Triton/vLLM, feature stores.

·        Hybrid deployment - on‑prem, cloud, edge, air‑gapped.

·        DevOps: Kubernetes, Docker, CI/CD, IaC.

·        Natural mentor; global collaboration experience.

·        Strong stakeholder communication; proven delivery of complex projects.

·        Comfortable with ambiguity and end‑to‑end ownership.

·        Pioneering in emerging AI

·        Customer‑centric

·        Pragmatic innovator

·        Collaborative

·        Growth‑oriented

·        Open‑source contributions

·        Published research/blogs/talks in GenAI/Agentic AI

·        Multi‑modal AI experience

·        Industrial/automation/IoT AI familiarity