Lead AI 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, GCP, 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