Senior AI Engineer – GenAI, Agentic AI & Machine Learning

We are hiring

Senior AI Engineer – GenAI, Agentic AI & Machine Learning

You lead the development of next-generation AI solutions, including Generative AI, Agentic AI, and Machine Learning, from prototype to production.

Bridging research and real-world impact, you also mentor junior engineers while working with LLMs and autonomous AI systems.

🔧 Your Responsibilities

GenAI & Agentic AI Development

• Design and build autonomous AI agent systems – multi-agent orchestration, tool-use frameworks, planning & reasoning architectures (e.g., ReAct, Plan-and-Execute, LangGraph, CrewAI, AutoGen)
• Architect production-grade GenAI applications – RAG pipelines, fine-tuning strategies, prompt engineering, guardrails, and evaluation frameworks
• Push the boundaries of LLM integration – function calling, structured outputs, multi-modal models, embedding strategies, and vector database design (Pinecone, Weaviate, Qdrant, pgvector)
• Implement agentic workflows that autonomously reason, retrieve, decide, and act across enterprise systems and data sources
• Evaluate and benchmark GenAI/agent solutions rigorously – hallucination detection, faithfulness metrics, latency optimization, cost management

Classical Machine Learning & Data Science

• Develop and deploy traditional ML models – classification, regression, time-series forecasting, anomaly detection, NLP, and computer vision for industrial use cases
• Build end-to-end ML pipelines – feature engineering, model training, hyperparameter optimization, validation, and serving
• Apply the right tool for the job – know when classical ML outperforms GenAI and vice versa; design hybrid solutions that combine both paradigms effectively
• Champion data quality and feature store practices to ensure reliable, reproducible model performance

MLOps & Production Engineering

• Own the AI/ML infrastructure – design scalable MLOps pipelines, CI/CD workflows, model registries, and automated retraining loops
• Deploy across hybrid environments – on-premises, cloud (AWS/Azure/GCP), edge, and air-gapped setups with equal confidence
• Implement production-grade observability – model monitoring, drift detection, A/B testing, logging, and alerting
• Leverage DevOps best practices – Kubernetes, Docker, infrastructure-as-code (Terraform/Ansible), GitHub Actions/GitLab CI

Mentoring & Collaboration

• Guide and support junior engineers – conduct code reviews, pair programming sessions, and share best practices to elevate the team's AI/ML capabilities
• Act as a technical sparring partner – help less experienced colleagues navigate complex architectural decisions and debug challenging problems
• Coordinate globally – work as a technical counterpart between Pune engineering and headquarters product/architecture teams
• Translate business needs into AI solutions – contribute to AI strategy, roadmaps, and technical decision-making alongside the Head of Digital
• Share knowledge actively – drive tech talks, documentation, and a culture of continuous learning within the team
• Represent Pune engineering expertise in global architecture reviews and technology forums

🚀 What You Bring

LLM Mastery

Production experience with GPT-4/Claude/Gemini/Llama/Mistral – fine-tuning, RLHF concepts, quantization, prompt engineering at scale

Agentic AI

Hands-on with agent frameworks (LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel) – multi-agent systems, tool integration, memory management

RAG Architectures

Advanced retrieval strategies – hybrid search, reranking, chunking optimization, multi-index routing, evaluation (RAGAS, DeepEval)

Vector Databases

Production deployment of Pinecone, Weaviate, Qdrant, Milvus, or pgvector

Guardrails & Safety

Experience with output validation, content filtering, hallucination mitigation, and responsible AI practices

Evaluation

Systematic LLM/agent evaluation – automated benchmarks, human-in-the-loop feedback, cost-performance trade-off analysis

Classical ML & Data Science (Strong Foundation)

Core ML

Proven track record with supervised/unsupervised learning, ensemble methods, deep learning (PyTorch/TensorFlow)

Industrial Use Cases

Experience in anomaly detection, predictive maintenance, time-series, NLP, or computer vision

Experimentation

Rigorous approach to hypothesis testing, A/B testing, and model validation

Hybrid Thinking

Ability to architect solutions that combine GenAI with classical ML where each adds the most value

Engineering & Infrastructure

• 5+ years in IT, with 2 years focused on AI/ML
• Programming: Strong Python (must-have); Go, Java, or Rust a plus
• MLOps: End-to-end pipeline experience – experiment tracking (MLflow/W&B), model serving (TorchServe/Triton/vLLM), feature stores
• Hybrid Deployment: Not cloud-only – proven experience with on-premises, edge computing, or air-gapped environments
• DevOps: Kubernetes, Docker, CI/CD, infrastructure-as-code

Communication & Teamwork

• Natural mentor – enjoys sharing knowledge, giving constructive feedback, and helping others grow technically
• Global collaboration – experience in distributed teams across time zones on headquarters-level projects
• Stakeholder communication – translate complex AI concepts for technical and non-technical audiences
• Delivery track record – complex projects, on time, with cross-functional dependencies
• Autonomous driver – comfortable navigating ambiguity and owning initiatives end-to-end

Interested candidates can apply: hr@schmalz.co.in

Explore Our Company: https://www.schmalz.com