We are seeking a highly skilled AI engineers with Infra and support experience..

Responsibilities include but are not limited to:

Design, provision, configure, and maintain scalable AI infrastructure on AWS to support AI/ML applications across development, testing, and production environments.

- Deploy, manage, and troubleshoot AI/ML models, LLM applications, inference endpoints, and APIs in production environments.
- Implement monitoring, logging, alerting, and observability for AI applications using AWS CloudWatch and other monitoring tools to ensure system availability and performance.
- Provide L2/L3 production support for AI applications, investigate incidents, perform root cause analysis, and implement corrective and preventive actions.
- Manage cloud infrastructure provisioning and configuration using Terraform/Scalr, ensuring secure, consistent, and repeatable deployments.
- Troubleshoot infrastructure, application, networking, API integration, containerization, and model inference issues to minimize service disruptions.
- Implement and maintain CI/CD pipelines for automated AI application deployment, testing, version control, release management, and rollback procedures.
- Monitor AI workloads for latency, throughput, resource utilization, error rates, and operational costs, implementing performance and cost optimization measures.
- Support containerized AI applications using Docker and Kubernetes, including Amazon EKS/ECS, with a focus on scalability, availability, and reliability.
- Maintain operational runbooks, incident reports, support documentation, and disaster recovery procedures while collaborating with DevOps, platform, security, and application support teams to meet SLAs.

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.
- Solid 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

- Robust 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

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