Senior Manager - AI Engineering

We are seeking a seasoned AI Architect and Engineering Leader with proven expertise in end-to-end AI solution development, deployment, and architecture across multiple domains.

In this dual-capacity role, you will be responsible for driving the AI technology strategy, leading execution, and managing a team of AI engineers and architects.

This role blends hands-on Technical Leadership with Strategic Delivery ownership — ensuring innovation, scalability and client confidence across a growing portfolio of AI initiatives. It goes beyond building models — it's about anchoring BOT's AI practice, scaling talent and ensuring enterprise-grade outcomes from design to production.

Key Responsibilities

Architecture & Technical Leadership

Define, design, and validate scalable, secure and high-performance AI architectures aligned with business goals and using AWS Bedrock, SageMaker, Lambda, Step Functions and other AWS-native services.

Drive full-lifecycle AI solutioning — data acquisition, feature engineering, model training, evaluation, deployment and monitoring.

Evaluate existing AI and data infrastructure; identify gaps and propose modernization or optimization strategies.

Lead proof-of-concepts and prototyping to assess new algorithms, frameworks and cloud AI services..

Evaluate and integrate LLM orchestration frameworks (LangChain, LangGraph, Strands, AgentCore) for multi-agent and context-aware systems..

Guide MLOps and AIOps pipelines — model versioning, retraining, and monitoring using SageMaker, MLflow and custom APIs.

Establish standards, frameworks, and reusable components for AI model deployment, inference and governance.

Champion MLOps best practices — CI / CD for ML, model registry, drift detection and automated retraining.

Oversee AI system integration with cloud platforms (AWS, Azure, GCP), APIs and enterprise data systems.

Guide architecture reviews and code quality checks to ensure technical excellence, maintainability and cost efficiency.

Management & Team Leadership

The leader will be both a technical visionary and a cultural steward, ensuring that India is not just an offshore hub, but a strategic extension of Caylent.

As a leader, you will work as an anchor who will build the India Caylent GDC.

Recruit, mentor, and manage a multidisciplinary team of AI engineers, ML scientists, data engineers, and MLOps specialists.

Define team goals, KPIs and career growth paths aligned with organizational strategy.

Allocate resources effectively, manage delivery timelines, and resolve technical dependencies or conflicts.

Foster a culture of innovation, continuous learning and responsible experimentation.

Conduct technical reviews, architecture sign-offs, and delivery governance for all AI engagements.

Build partnerships across Cloud, Data, and Product teams to drive cross-functional collaboration.

Represent the team in leadership forums; report delivery metrics, risks, and roadmap updates.

Operational Excellence & Governance

Define and implement AI governance standards - model lifecycle management, documentation, explainability, and bias mitigation.

Ensure compliance with security and regulatory frameworks (GDPR, HIPAA, SOC2, EU AI Act, etc.) as relevant.

Establish monitoring and observability for AI pipelines - model drift, data quality, latency, and cost.

Drive AI FinOps - monitor compute utilization, optimize GPU / instance costs, and enforce budget accountability.

Implement disaster-recovery and rollback strategies for production AI deployments.

Champion ethical and responsible AI - establish review boards and feedback loops for transparency and fairness.

Set up metrics dashboards (accuracy, precision, inference speed, ROI) to assess solution impact.

Stakeholder Engagement & Strategy

Collaborate with business and product leadership to align AI strategy with organizational objectives.

Serve as the client-facing technical authority in AI engagements — ensuring solution alignment and delivery confidence.

Translate complex AI capabilities into clear business outcomes and client-facing value propositions.

Contribute to pre-sales and solutioning for AI projects; contribute to proposals, estimations and architecture reviews.

Serve as the AI thought leader and advisor to global teams, shaping enterprise-wide AI initiatives.

Work with global leadership to evolve AI CoE capabilities — frameworks, accelerators and reusable assets.

Continuously evaluate AI industry trends (GenAI platforms, agentic workflows, domain-specific models) and lead internal adoption pilots.

Requirements

Qualifications & Skills

Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science or related field.

8+ years of total experience, with at least 5+ years in AI / ML solution architecture and delivery leadership.

Proven experience leading end-to-end AI projects from design to production at scale.

Deep understanding of machine learning, deep learning and MLOps workflows.

Strong command of Python and modern AI / ML libraries (TensorFlow, PyTorch, Scikit-learn).

Hands-on with cloud-native AI services — AWS (Bedrock, SageMaker, Lambda, Step Functions), Azure ML, or GCP Vertex AI.

Experience in microservice-based AI deployment, API design, and serverless or containerized inference (ECS / Fargate / Kubernetes).

Working knowledge of data engineering pipelines, ETL orchestration and vector / feature stores.

Familiarity with LLM and GenAI concepts (prompt engineering, RAG, embeddings) preferred but not mandatory.

Strong understanding of software engineering and architecture principles - design patterns, scalability, fault tolerance and security.

Demonstrated ability to make technical trade-offs and lead delivery under ambiguity.

Excellent communication, people-management, and stakeholder-handling skills.

Certifications : Preferred – AWS Certified Machine Learning (Specialty), AWS Solutions Architect (Professional) or equivalent.

Prior consulting or CoE leadership experience in AI / ML is an advantage.

Signs You May Be a Great Fit

Impact : Play a pivotal role in shaping a rapidly growing venture studio with Cloud-driven digital transformation.

Culture : Thrive in a collaborative, innovative environment that values creativity, ownership, and agility.

Growth : Access professional development opportunities and mentorship from experienced peers.

Benefits : Competitive salary, wellness packages, and flexible work arrangements that support your lifestyle and goals.

Skills Required

Step Functions, Tensorflow, Azure ML, Lambda, Pytorch, ECS, Kubernetes, Python