Engineering Manager - AI/ML Platform

Job Description :

We are seeking an experienced and highly motivated Engineering Manager AI/ML Platform to lead the design, development, and delivery of enterprise-scale AI/ML platforms and Generative AI solutions.

The ideal candidate will have a strong background in software engineering, cloud-native architectures, MLOps, and machine learning systems, coupled with proven leadership experience managing high-performing engineering teams.

This role requires driving strategic AI initiatives, including Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, MLOps automation, and scalable platform engineering while collaborating closely with Product, Data Science, Architecture, and Business stakeholders.

Key Responsibilities :

- Lead, mentor, and grow a team of AI/ML engineers, platform engineers, and software developers.

- Define engineering best practices, coding standards, and architectural guidelines.

- Conduct technical reviews, performance evaluations, and career development planning.

- Foster a culture of innovation, ownership, collaboration, and continuous learning.

- Drive Agile development processes and ensure timely delivery of business-critical initiatives.

- Design, develop, and maintain scalable AI/ML platforms supporting model development, training, deployment, and monitoring.

- Build reusable frameworks and platform capabilities for machine learning lifecycle management.

- Establish standardized workflows for model experimentation, validation, deployment, and governance.

- Develop enterprise-grade solutions supporting predictive analytics, machine learning, and Generative AI use cases.

- Lead implementation of GenAI applications leveraging Large Language Models (LLMs).

- Design and develop Retrieval-Augmented Generation (RAG) pipelines for enterprise knowledge systems.

- Build AI Agent architectures using frameworks such as LangChain, LangGraph, CrewAI, or similar technologies.

- Optimize prompt engineering, model orchestration, and inference performance.

- Evaluate and integrate foundation models from OpenAI, Anthropic, Meta, Google, and open-source ecosystems.

- Implement MLOps best practices for continuous training, deployment, monitoring, and retraining.

- Build automated ML pipelines using MLflow, Kubeflow, Airflow, or equivalent platforms.

- Establish model versioning, experiment tracking, feature management, and governance frameworks.

- Implement monitoring solutions for model drift, performance degradation, and operational reliability.

- Ensure reproducibility and scalability of machine learning workloads.

- Design cloud-native architectures on AWS, Azure, or Google Cloud Platform.

- Develop containerized applications using Docker and Kubernetes.

- Build scalable microservices-based systems supporting AI/ML workloads.

- Implement event-driven architectures leveraging Kafka and messaging platforms.

- Ensure high availability, fault tolerance, security, and performance optimization.

- Partner with Product Managers to define AI product roadmaps and technical strategies.

- Collaborate with Data Scientists to operationalize machine learning models.

- Work closely with Enterprise Architects to align platform capabilities with organizational objectives.

- Present technical solutions and platform strategies to senior leadership and stakeholders.

- Drive adoption of AI/ML best practices across engineering teams.

Required Skills & Technical Expertise :

1. Programming Languages :

- Strong proficiency in Python and Java.

- Experience with REST APIs and backend application development.

2. Generative AI & Machine Learning :

- Large Language Models (LLMs), Generative AI (GenAI), Retrieval-Augmented Generation (RAG), Prompt Engineering, LangChain / LangGraph, AI Agents, Fine-tuning and model optimization, and Vector databases (Pinecone, Weaviate, ChromaDB, FAISS).

3. MLOps & ML Platforms :

- MLflow, Kubeflow, Airflow, Model Monitoring, Experiment Tracking, Feature Stores, and CI/CD for ML workflows.

4. Cloud Technologies :

- AWS (SageMaker, EKS, Lambda, Bedrock), Microsoft Azure (Azure ML, AKS, OpenAI Services), and Google Cloud Platform (Vertex AI, GKE).

5. Containerization & Orchestration :

- Docker, Kubernetes, Helm, and Container Security.

6. Streaming & Messaging :

- Apache Kafka, Event-driven Architecture, and Message Queues.

7. DevOps & Automation :

- CI/CD Pipelines, GitHub Actions, Jenkins, GitLab CI/CD, and Infrastructure as Code (Terraform preferred).

8. Databases :

- PostgreSQL, MySQL, MongoDB, Redis, and Vector Databases.

Qualifications :

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

- 9- 15 years of overall software engineering experience.

- 3+ years of experience leading engineering teams.

- Strong experience building enterprise AI/ML platforms and cloud-native applications.

- Proven expertise in MLOps, Generative AI, and Large Language Model-based solutions.

- Experience delivering scalable, production-grade machine learning systems.

- Strong understanding of distributed systems and microservices architecture.

- Experience with OpenAI, Anthropic, Gemini, Llama, Mistral, or similar foundation models.

- Exposure to AI governance, Responsible AI, and model compliance frameworks.

- Experience in enterprise AI transformation initiatives.

- Cloud certifications (AWS, Azure, GCP) preferred.

- Kubernetes or MLOps certifications are a plus.