Senior / Lead Machine Learning Engineer (AI/ML)

Job Description – Senior / Lead Machine Learning Engineer (AI/ML)

Company: Saradysol Tech Ventures Private Limited

Experience: 15+ years; exceptional candidates with equivalent expertise considered

Location: Remote

Employment Type: Contract

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.

Preferred Experience

• Enterprise Generative AI applications and reliable agent or tool-calling workflows.
• Model fine-tuning, inference optimization, and structured AI evaluation.
• Distributed data processing, large-scale ML systems, or Kubernetes.
• Hugging Face, LangChain, LlamaIndex, or comparable tools.
• Direct customer engagement and presentations to senior stakeholders.

Why Join Us?

• Build production-ready Machine Learning, Generative AI, and LLM solutions that solve real business problems.
• Take end-to-end ownership of AI initiatives and influence architecture and technical decisions.
• Lead technical discussions, mentor engineers, and share knowledge across teams.
• Work in a collaborative and inclusive environment that values initiative, accountability, and continuous learning.

How to Apply?

Send your updated resume to careers@saradysol.com with the subject line:“Senior / Lead Machine Learning Engineer (AI/ML)”Alternatively, apply via LinkedIn Easy Apply.Join Saradysol in building next-generation AI/ML platforms and analytics solutions!