Generative AI Engineer

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 will focus 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 strong hands-on experience in 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 rapidly 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, capable of translating business problems into practical AI solutions. This role will also be responsible for ensuring that AI solutions are production-ready, observable, cost-efficient, and aligned with enterprise governance, risk, and compliance expectations.

Responsibilities include but are not limited to:

• Design, develop, and deploy enterprise AI solutions using LLMs, Prompt Engineering, Agentic AI frameworks, classical Machine Learning, and Data Science techniques .
• Build intelligent applications such as 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 understand business requirements, define AI opportunities, structure hypotheses, and translate them into scalable technical solutions.
• Build and manage AI solution infrastructure on AWS , leveraging services such as S3, Lambda, EKS/ECS, API Gateway, IAM, CloudWatch, RDS, DynamoDB, OpenSearch, Bedrock , and other approved cloud services as applicable.
• Provision and manage infrastructure using Terraform/Scalr , ensuring secure, repeatable, and scalable deployments across environments.
• Implement observability, evaluation, monitoring, feedback loops, and guardrails for enterprise AI applications to ensure quality, safety, and continuous improvement.
• Partner with architecture, platform, DevOps, security, and governance teams to ensure AI solutions meet enterprise standards for security, compliance, resiliency, and operational support.
• Lead technical design discussions, code reviews, solution architecture decisions, and engineering best practices for AI application development.
• Support MLOps / LLMOps capabilities including CI/CD, model and prompt versioning, automated testing, deployment automation, runtime monitoring, and production incident response.
• Communicate technical findings, model behaviour, solution risks, and business impact clearly to leadership, stakeholders, and partner teams.
• Mentor junior engineers and data scientists while contributing to reusable frameworks, accelerators, and standards for AI engineering across the firm.

Required Skills

• Bachelor’s degree in computer science, Engineering, Data Science, Artificial Intelligence , or a related technical field.
• hands-on experience in Python and software development for AI, ML, data engineering, or cloud-based applications.
• experience in Machine Learning / Data Science , including feature engineering, model training, experimentation, evaluation, and deployment.
• experience in LLMs / Generative AI use cases such as **prompt engineering, prompt optimizat…