Artificial Intelligence Engineer

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.
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

Work arrangement
No