We're building the ML and agentic AI layer that decides what deserves an analyst's attention — and we need a player-coach to lead it.

This isn't a management-only seat. You'll set technical direction for a team of AI/ML engineers while staying hands-on with the hardest problems yourself — spanning classical ML (classification, ranking, clustering, anomaly detection) and LLM-driven agentic reasoning on top of it.

You'll own:

? The ML layer — alert classification, case prioritization, behavioral baselining, feature engineering over security telemetry

? Agentic workflows — retrieval-grounded investigation, tool use, structured output

? Model decisions — what runs where, local/self-hosted deployment for private-cloud & sovereign customers, inference cost/latency/quality at scale

? Evaluation & trust — harnesses, drift detection, guardrails, agent autonomy boundaries

What you bring:





✅ 8 years in ML/applied AI/data science, 4 owning production systems

✅ 2 years leading or tech-leading an AI/ML team, still hands-on

✅ Proven ownership of ML agentic AI in production (not just prototypes)

✅ Robust RAG, agent orchestration, prompt engineering, fine-tuning

✅ Hands-on with vLLM/TGI/Ollama — quantization, GPU sizing, throughput/cost trade-offs

✅ Python (PyTorch/TensorFlow/scikit-learn), SQL, MLOps on AWS/GCP/Azure with Docker/K8s

Bonus: UEBA, graph analytics, MITRE ATT&CK;/ATLAS familiarity, publications or open-source contributions.

If you've shipped both the "narrow the signal" and "reason over it" halves of this problem — and want real ownership over which — let's talk. Write to [email protected]

📌 Lead AI Engineer - "Local-first LLMs, real production ownership" - Pune
🏢 CareerXperts Consulting
📍 Pune