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.