Senior / Lead Machine Learning Engineer (AI/ML)

Job Description – Senior / Lead Machine Learning Engineer (AI/ML) Company: Saradysol Tech Private Limited Experience: 15+ years; exceptional candidates with equivalent expertise considered Location: Bengaluru / Remote / Hybrid, based on business requirements Employment Type: Full-Time About the Role We are looking for a hands-on Senior / Lead Machine Learning Engineer who combines strong AI/ML expertise with excellent communication, technical leadership, mentoring skills, and end-to-end ownership. You will translate business problems into practical AI solutions, design architecture, develop production-ready systems, and guide engineers throughout delivery. The ideal candidate can work independently, make informed decisions, explain complex concepts clearly, and take responsibility for results. Key Responsibilities 1. End-to-End Ownership and Delivery - Own AI/ML initiatives from requirement gathering and feasibility assessment through development, deployment, monitoring, and continuous improvement. - Translate business objectives into clear technical requirements, delivery plans, and measurable success criteria. - Take responsibility for solution quality, technical decisions, timelines, and production performance. - Proactively identify risks, dependencies, and blockers, and drive their resolution. - Communicate progress transparently and escalate issues early with proposed solutions. 2. Machine Learning and Generative AI Development - Design, develop, evaluate, and optimize machine learning and deep learning solutions. - Apply appropriate techniques for data preparation, feature engineering, model selection, validation, and performance evaluation. - Build applications using LLMs, retrieval-augmented generation (RAG), embeddings, vector databases, and agent workflows where appropriate. - Select suitable approaches based on business needs, data availability, accuracy, latency, cost, and maintainability. - Establish evaluation baselines, prevent data leakage, and document assumptions and model limitations. - Assess emerging AI technologies and apply them where they offer practical value. 3. Production Engineering and MLOps - Write clean, modular, maintainable, and well-tested Python code. - Build APIs and integrate AI capabilities into enterprise applications. - Implement reproducible pipelines, experiment tracking, model versioning, automated testing, and CI/CD. - Deploy and operate solutions using cloud services, Docker, and appropriate MLOps tools. - Monitor model quality, data drift, reliability, inference latency, and operating costs. - Troubleshoot production issues and implement sustainable improvements. - Apply appropriate security, privacy, access-control, and responsible AI practices. 4. Communication and Stakeholder Collaboration - Explain complex AI/ML concepts clearly to technical and non-technical audiences. - Lead requirement discussions and ask questions that clarify business needs and expectations. - Present architecture proposals, evaluation results, technical recommendations, and delivery updates professionally. - Communicate trade-offs, risks, uncertainty, and limitations openly. - Write clear technical documentation, architecture explanations, and decision records. - Collaborate effectively with software engineers, data engineers, DevOps teams, product teams, customers, and management. 5. Technical Leadership, Mentoring, and Teaching - Provide hands-on technical direction through architecture discussions, code reviews, and problem-solving sessions. - Mentor junior and mid-level engineers with practical guidance and constructive feedback. - Explain both how to implement solutions and why specific approaches are appropriate. - Conduct technical training, knowledge-sharing sessions, and architecture walkthroughs. - Establish coding standards, engineering practices, and production-readiness expectations. - Lead through expertise, evidence, and collaboration, including without formal people-management responsibility. Required Technical Skills - Programming: Strong Python, SQL, debugging, data structures, and software engineering fundamentals. - Machine Learning: Supervised and unsupervised learning, feature engineering, model selection, statistical foundations, and evaluation. - Deep Learning: Hands-on experience with PyTorch or TensorFlow. - Generative AI: LLM applications, RAG, embeddings, vector search, prompt design, and output evaluation. - Frameworks: scikit-learn and relevant AI libraries. - Data Pipelines: Data preprocessing, validation, feature pipelines, and reproducible workflows. - MLOps: Experiment tracking, model versioning, deployment, and monitoring using MLflow or equivalent. - Deployment: REST APIs, FastAPI or equivalent, Docker, Git, automated testing, and CI/CD. - Cloud: Hands-on experience with at least one of AWS, Azure, or GCP. - System Design: Scalable architecture, observability, reliability, security, and performance optimization. Essential Professional Qualities - Excellent communication: Speaks and writes clearly, listens actively, and explains complex topics simply. - Strong ownership: Drives work to completion and follows through without constant follow-up. - Technical leadership: Guides engineers, makes sound decisions, and leads by example. - Teaching mindset: Explains concepts patiently and helps others develop independent problem-solving skills. - Structured problem-solving: Investigates root causes and evaluates alternatives using evidence. - Proactive approach: Anticipates challenges and acts before they affect delivery. - Accountability: Honors commitments and communicates early when priorities or timelines change. - Business understanding: Connects technical decisions to measurable business outcomes. - Professional maturity: Accepts feedback and handles disagreements respectfully. Qualifications and Experience - Bachelor’s or Master’s degree in Computer Science, AI, Machine Learning, Data Science, Engineering, or equivalent practical experience. - Demonstrated experience developing, deploying, and supporting production AI/ML systems. - End-to-end ownership of at least one substantial AI/ML initiative, with clear evidence of individual contribution. - Experience making architecture decisions and explaining technical and business trade-offs. - Evidence of mentoring engineers, leading technical initiatives, or conducting technical training. - Ability to work independently while collaborating effectively across teams.