Artificial Intelligence Engineer

AI Engineer

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 optimization, RAG, embeddings, vector databases, evaluation frameworks, and guardrails

  .
• Strong experience with

  Prompt Engineering

  , including prompt design patterns, prompt chaining, instruction design, context-window optimization, role/task prompting, structured response generation, and prompt tuning for enterprise use cases.
• 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 developing, deploying, and managing scalable 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 in enterprise environments.
• Experience with

  SQL and NoSQL databases

  , vector databases, and data integration across structured and unstructured sources.
• Strong knowledge of software engineering fundamentals including version control, testing, CI/CD, secure coding, code reviews, and release management.
• Experience presenting technical solutions, results, and recommendations to business stakeholders and leadership.
• Strong analytical thinking, problem-solving, communication, and cross-functional collaboration skills.

Preferred Skills

• Experience in

  financial services, investment management, compliance, risk, or other regulated enterprise environments

  .
• Experience with

  MLOps / LLMOps

  tools and practices for deployment, monitoring, evaluation, and lifecycle management of AI systems.
• Familiarity with frameworks and tools such as

  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 domain-specific 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 strong hands-on experience in end-to-end model development and deployment.
• Solid data engineering background with experience in building and managing complex, large-scale data systems.
• Proven ability to work effectively in large, collaborative teams within 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 with focus on emerging AI technologies and best practices