AI Engineer

Job Title

Senior AI Engineer – Generative AI & LLMs

Overview / Summary

We are seeking a highly skilled AI Engineer to design, develop, and deploy enterprise-scale AI solutions that solve complex business problems across the organization. This role focuses on Generative AI, Large Language Models (LLMs), Prompt Engineering, Agentic AI systems, Machine Learning, Data Science, and cloud-native engineering on AWS.

The ideal candidate will have hands-on experience building intelligent applications end to end, including data preparation, model development, prompt design and optimization, orchestration of agentic workflows, deployment automation, infrastructure provisioning, monitoring, and production support. This role requires close collaboration with business stakeholders, data teams, architects, platform teams, and engineering partners to advance AI adoption in a secure, scalable, and business-aligned manner.

The successful candidate will act as a problem solver, trusted consultant, and technical leader, translating business problems into practical AI solutions while ensuring solutions are production-ready, observable, cost-efficient, and aligned with enterprise governance, risk, and compliance expectations.

Key Responsibilities

• Design, develop, and deploy enterprise AI solutions using LLMs, Prompt Engineering, Agentic AI frameworks, Machine Learning, and Data Science techniques
• Build intelligent applications including RAG-based systems, AI copilots, conversational assistants, autonomous or semi-autonomous agent workflows, document intelligence solutions, and NLP-driven applications
• Design and optimize prompts for enterprise use cases, including prompt templates, chaining strategies, structured outputs, instruction tuning patterns, context control, and response optimization
• Develop and maintain data pipelines for structured and unstructured data to support model training, retrieval, inference, analytics, and evaluation workflows
• Implement and manage retrieval pipelines, embeddings, vector search, tool calling, memory strategies, and orchestration logic for LLM-powered applications
• Fine-tune, evaluate, optimize, and monitor AI/ML models and LLM-based systems for performance, accuracy, explainability, scalability, reliability, and cost efficiency
• Collaborate with business stakeholders and cross-functional teams to define AI opportunities and translate business requirements into scalable technical solutions
• Build and manage AI solution infrastructure on AWS using services such as S3, Lambda, EKS/ECS, API Gateway, IAM, CloudWatch, RDS, DynamoDB, OpenSearch, Bedrock, and related cloud services
• Provision and manage infrastructure using Terraform and/or Scalr for secure and scalable deployments
• Implement observability, evaluation, monitoring, feedback loops, and guardrails for enterprise AI applications
• Partner with architecture, platform, DevOps, security, and governance teams to ensure compliance with enterprise standards
• Lead technical design discussions, code reviews, architecture decisions, and engineering best practices
• Support MLOps and LLMOps capabilities including CI/CD, model and prompt versioning, automated testing, deployment automation, runtime monitoring, and production incident response
• Communicate technical findings, model behavior, risks, and business impact to leadership and stakeholders
• Mentor junior engineers and data scientists while contributing reusable frameworks, accelerators, and AI engineering standards

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related technical field
• 5+ years of hands-on experience in Python and software development for AI, ML, data engineering, or cloud-based applications
• 3+ years of experience in Machine Learning and Data Science, including feature engineering, model training, experimentation, evaluation, and deployment
• 2+ years of experience with LLMs and Generative AI use cases including prompt engineering, RAG, embeddings, vector databases, evaluation frameworks, and guardrails
• Strong experience with Prompt Engineering, including prompt design patterns, prompt chaining, instruction design, context-window optimization, structured response generation, and prompt tuning
• Strong understanding of Agentic AI patterns including multi-step reasoning workflows, tool usage, memory/context handling, orchestration frameworks, and autonomous task execution
• Strong experience with AWS cloud services for AI/ML and data-driven applications
• Hands-on experience with Terraform and/or Scalr for infrastructure as code and environment provisioning
• Experience with APIs, microservices, and containerized deployments
• Experience with SQL and NoSQL databases, vector databases, and data integration across structured and unstructured data sources
• Strong knowledge of software engineering fundamentals including version control, testing, CI/CD, secure coding, code reviews, and release management
• Experience presenting technical solutions and recommendations to business stakeholders and leadership
• Strong analytical thinking, problem-solving, communication, and collaboration skills

Preferred Qualifications

• Experience in financial services, investment management, compliance, risk, or regulated enterprise environments
• Experience with MLOps and LLMOps tools and practices
• Familiarity with LangChain, LangGraph, LlamaIndex, Hugging Face, OpenAI APIs, Amazon Bedrock, MLflow, Airflow, Spark, or similar technologies
• Experience designing and optimizing prompts for RAG workflows, agentic systems, tool calling, reasoning tasks, workflow automation, and enterprise AI applications
• Experience with semantic search, vector retrieval, document intelligence, conversational AI, and knowledge-grounded AI systems
• Knowledge of Responsible AI, model governance, explainability, bias detection, risk controls, and enterprise AI safety practices
• Experience working in Agile/Scrum delivery models
• Experience leading or mentoring teams and driving implementation across multiple stakeholders
• Advanced expertise in AI/LLMs with hands-on experience in end-to-end model development and deployment
• Strong data engineering experience building and managing large-scale data systems
• Ability to work effectively in collaborative, fast-paced enterprise environments

Key Competencies

• Strong ownership and accountability for end-to-end delivery
• Ability to translate ambiguous business problems into practical AI solutions
• Strong collaboration across engineering, product, business, and governance teams
• Technical leadership in solution design and implementation
• Strong communication and stakeholder management skills
• Continuous learning mindset focused on emerging AI technologies and best practices

#LI-Hybrid #LI- SB1