AI/ML Senior Software Engineer

About the Role 

We're looking for a Lead AI Engineer who designs and delivers production-grade AI/ML solutions for complex manufacturing environments — and who treats AI-assisted development as a default, not an experiment. You'll lead the architecture of large-scale ML systems, drive LLM and agentic AI adoption across engineering and operations teams, and own the end-to-end lifecycle from research to deployment. You'll work with stakeholders across the globe to translate messy, high-stakes manufacturing problems into reliable, scalable AI systems. 

This is a hands-on AI engineering leadership role. You'll architect, you'll ship, and you'll raise the bar for how your team builds with and builds on AI. 

What You'll Do 

• Lead the design, development, and deployment of AI/ML solutions targeting manufacturing processes — yield optimization, predictive maintenance, equipment diagnostics, process control, and field troubleshooting. 

• Architect and fine-tune large-scale LLMs for domain-specific tasks including text generation, summarization, question answering, and knowledge retrieval. 

• Design and implement agentic AI workflows, RAG pipelines, and multi-agent systems to automate complex engineering and operational tasks. 

• Use AI-assisted coding tools (Claude Code, GitHub Copilot, etc.) as a core part of your daily workflow — across design, implementation, testing, debugging, and documentation. 

• Establish team-wide AI engineering practices — prompt libraries, reusable agent skills, evaluation frameworks, and guardrails for AI-generated outputs — so the team gets compounding leverage. 

• Apply statistical rigor to model evaluation, experiment design, and data analysis — ensuring solutions are defensible, not just demo-ready. 

• Drive MLOps maturity — CI/CD for models, versioning, monitoring, drift detection, and observability across deployed AI systems. 

• Align and manage multiple stakeholders across global teams spanning different regions, time zones, and cultural contexts — translating technical depth into business clarity. 

• Own features and systems end-to-end, from problem framing through deployment and iteration, collaborating with domain experts, product teams, and infrastructure engineers. 

• Mentor junior engineers on both AI/ML fundamentals and effective AI-augmented development practices. Set quality bars for code, experimentation, and production readiness. 

What We're Looking For 

Required 

• 10+ years of IT experience, with 6+ years focused on data science and AI engineering. 

• B.E./B.Tech/M.Tech/MCA in Computer Science, Electronics & Communication, or a related field. Master's degree in Computer Science, AI, Deep Learning, or NLP is highly preferred. 

• Proven experience building and deploying AI/ML solutions for manufacturing processes in the high-tech industry. 

• Strong Python programming skills; hands-on experience with TensorFlow, PyTorch, or Keras. 

• Solid foundation in statistics — hypothesis testing, experimental design, regression, statistical process control — and the ability to apply it rigorously to model evaluation and data analysis. 

• Deep understanding of transformer architectures (BERT, GPT, LLaMA) and fine-tuning techniques (LoRA, adapters, prompt engineering). 

• Experience with LLM orchestration frameworks such as LangGraph, LangChain, or LlamaIndex — including agentic workflows, RAG pipelines, and multi-agent system design. 

• Proficiency in AI-assisted coding tools (e.g., Claude Code, GitHub Copilot) for accelerating development across complex codebases. 

• Experience with MLOps best practices including CI/CD and model versioning. 

• Experience working with cloud platforms, with a preference for the Azure ecosystem. 

• Strong analytical, problem-solving, and communication skills — written, verbal, and visual. 

• Track record of driving technical initiatives end-to-end across multiple global stakeholders, with demonstrated leadership and architectural ownership. 

Nice to Have 

• Semiconductor manufacturing domain experience. 

• Familiarity with Databricks, MLflow, and Delta Lake. 

• Proficiency in data preprocessing and handling large-scale text, image, or multimodal datasets. 

• Comfortable working in UNIX/Linux environments with command-line scripting. 

• Experience applying security-first principles to AI systems — threat modeling, data leakage prevention, prompt injection mitigation, and dependency hygiene. 

• Familiarity with observability practices for ML systems — structured logging, metrics, drift monitoring, and alerting. 

• Exposure to computer vision or multimodal models. 

• Open-source contributions, technical writing, or conference talks — especially on applied AI in industrial or manufacturing contexts.