AI/ml Engineer

Key Responsibilities
We are seeking an experienced Lead AI Engineer to lead strategic AI initiatives at a customer location. This role combines deep technical expertise in Generative AI, Agentic AI, Machine Learning, and AI Platform Engineering with the ability to engage senior stakeholders, define AI roadmaps, architect enterprise solutions, and lead delivery teams.
The successful candidate will act as the primary AI technical lead, driving the design, development, deployment, and operationalization of business-critical AI solutions while ensuring alignment with customer objectives and enterprise standards.


Required Qualifications
Minimum 8+ years of experience in machine learning engineering and AI development, including 2+ years in a technical lead or team lead capacity
Proven track record of leading ML/AI projects end-to-end and mentoring engineers
Demonstrated experience in client-facing or consulting roles, including managing senior stakeholders and serving as a technical advisor
Strong stakeholder-management skills: gathering and clarifying requirements, setting expectations, managing scope, and building long-term trust with customers
Excellent verbal and written communication, with the ability to present complex technical concepts and trade-offs to non-technical and executive audiences
Deep expertise in machine learning algorithms and techniques: supervised/unsupervised learning, deep learning, reinforcement learning, etc.
Solid experience in natural language processing (NLP): language models, text generation, sentiment analysis, etc.
Proven understanding of generative AI concepts: text/image/audio synthesis, diffusion models, transformers, etc.
Expertise in Agentic AI and building production-grade real-world applications using it
Hands-on experience with Agentic AI frameworks such as LangGraph, ADK, Autogen, etc.
Hands-on experience developing and deploying generative AI applications (text generation, conversational AI, image synthesis, etc.)
Strong experience in MLOps and ML model deployment pipelines, including defining standards and best practices for a team
Proficiency in Python and ML frameworks such as TensorFlow, PyTorch, etc.
Knowledge of cloud platforms (AWS, Google Cloud Platform, Azure) and tools for scalable ML solution deployment
Experience with data processing, feature engineering, and model training on large datasets
Familiarity with responsible AI practices, AI ethics, model governance, and risk mitigation
Strong grasp of software engineering best practices and their application to ML systems
Experience driving delivery within an agile development environment
Exposure to tools/frameworks for monitoring and analyzing ML model performance and data accuracy
Strong problem-solving and analytical abilities

Bachelor's or master's degree in Computer Science, AI, Statistics, Math, or related fields
Willingness and ability to work on-site at the client's office
Proven experience working in an onshore/offshore delivery model — coordinating distributed teams across geographies and time zones
Responsibilities
Client Engagement & Stakeholder Management
Serve as the primary technical point of contact and trusted advisor at the client's office
Engage senior client stakeholders to understand business goals, gather requirements, and shape the AI/ML roadmap
Manage scope, timelines, expectations, and risks; proactively communicate progress, trade-offs, and blockers
Translate ambiguous business problems into well-defined technical solutions and clear delivery plans
Present designs, outcomes, and recommendations to technical and executive audiences; influence decisions and build consensus
Identify opportunities to expand value delivered to the customer
Technical Leadership & Strategy
Define the technical vision, architecture, and roadmap for ML/AI initiatives across the client's domains
Set engineering standards and best practices for model development, MLOps, and responsible AI
Evaluate and select frameworks, tools, and platforms; make build-vs-buy and design trade-off decisions balancing performance, scalability, cost, and reliability
Team Leadership & Mentorship
Lead, mentor, and grow a team of ML/AI engineers; conduct design and code reviews
Plan, prioritize, and delegate work; track progress and remove blockers
Foster a culture of technical excellence, knowledge-sharing, and continuous learning
Delivery & Execution
Design, develop, and optimize machine learning models for applications across different domains
Oversee the build of NLP pipelines for tasks like text generation, summarization, translation, etc.
Drive the development and deployment of cutting-edge generative AI and Agentic AI applications
Establish and govern MLOps practices: model training, evaluation, deployment, monitoring, and maintenance
Integrate ML capabilities into existing products or lead the creation of new AI-powered applications
Ensure robust data mining, cleaning, preparation, and augmentation for training reliable ML models
Own model performance, scalability, and reliability across the production lifecycle
Innovation
Continuously research, evaluate, and introduce state-of-the-art AI/ML algorithms and techniques
Champion responsible AI, model governance, and risk mitigation across the team's work