AI​/ML Engineer

The AI/ML Engineer will play a crucial role in the AI Center of Excellence (CoE), supporting cross-functional teams across the organization. This position will focus on designing, developing and developing machine learning (ML), artificial intelligence (AI), Generative AI (GenAI) and Agentic AI solutions to address domain specific needs, improve user experiences and automate business workflows.

(The primary functions, scope, and responsibilities of the role)

• Work across diverse GenAI platforms like AWS, Salesforce, Oracle, Snowflake, MS Copilot, and other 3rd party GenAI platforms and libraries.
• Automate workflows involving extraction of complex, multimodal unstructured content from a variety of sources into highly accurate and reliable structured content using platforms like AWS Textract and Bedrock.
• Design and build MCP hosts, clients, and servers.
• Establish and use frameworks for automated LLM testing.
• Create regression test suites to detect drift or prompt breakage.
• Integrate with internal and external web services using secure authentication and authorization mechanisms.
• Adopt and ensure safe practices to protect against prompt injections, jailbreaks, and conform to enterprise security guidelines.
• Design, develop, and deploy production‑grade traditional ML models (e.g., regression, classification, clustering, recommender systems) for a variety of business use cases.
• Design, maintain, and optimize end‑to‑end AI/ML pipelines, including data ingestion, training, evaluation, deployment, and monitoring on cloud infrastructure (e.g., AWS or equivalent).
• Ensure AI/ML solutions are scalable, reliable, secure, and cost‑effective within cloud environments.
• Create reusable components, frameworks, and best practices to accelerate AI development.

• Design and develop GenAI solutions using prompt engineering, Context Engineering, Retrieval‑Augmented Generation (RAG), and custom pipelines.
• Design and develop interoperable AI agents using Model Context Protocol (MCP) and/or Google A2A.

• Partner with data scientists, architects, product managers, business stakeholders, and technical teams across the organization to align AI solutions with organizational goals.
• Provide hands‑on technical support and mentorship to technical teams across the enterprise.

Minimum qualifications needed for this position including education, experience, certification, knowledge, and/or physical requirements.

Knowledge of:

• Machine learning algorithms, deep learning frameworks, Cloud AI technologies, GenAI technologies, and emerging Agentic AI technologies.
• Cloud platforms (e.g., AWS, Azure, GCP) for scalable AI/ML development.
• Responsible AI principles, including bias mitigation and ethical deployment.
• ML Ops best practices including CI/CD for ML, model monitoring, and versioning.

Ability to:

• Build robust, scalable, and efficient AI/ML solutions in cloud‑native environments.
• Translate ambiguous business problems into clear, technical ML/AI tasks.
• Communicate complex ideas clearly to technical and non‑technical stakeholders.
• Learn and adapt quickly to emerging AI technologies, techniques, and tools.

Education and/or

Experience:

• Bachelor’s degree in computer science, engineering, or a related field.
• 3+ years of experience designing and deploying ML/AI solutions in real‑world environments, with strong proficiency in:
• Python and LLM APIs (OpenAI, Azure OpenAI, Gemini, Anthropic, etc.)
• Prompt engineering, context construction, and grounding strategies.
• Retrieval Augmented Generation (RAG) for extracting, chunking, and embedding unstructured documents from diverse sources.
• Building Model Context Protocol (MCP) clients, servers, and hosts.
• REST APIs and integration with internal/external APIs.
• Intelligent Document Processing and/or OCR technologies on complex documents.
• Google A2A.
• AWS services (Lambda, Bedrock, Step Functions, API Gateway, IAM).
• Observability tools like Dynatrace or similar GenAI observability tools.
• GenAI foundations and concepts.
• Enterprise data privacy, AI governance, and observability.
• Common ML/AI libraries (Tensor Flow, PyTorch, scikit‑learn).
• Data engineering, SQL, and feature engineering.
• Cloud services such as AWS Sagemaker, Lambda, ECS, S3, and IAM.
• Containerization (Docker) and orchestration (Airflow, Kubeflow).
• Version control and collaboration tools (Git, Jira, Confluence).

• Master’s in a related technical field.
• Hands‑on experience with agentic AI frameworks.
• Prior contributions to open‑source AI/ML projects or published research.
• AI/ML certifications from cloud providers.
• Experience in highly regulated industries (e.g., healthcare, finance) is a plus.