AI Engineer

Role Overview

We are seeking a Senior AI Engineer with at least four years of hands-on experience delivering production-grade AI and machine learning solutions. The role requires expertise in generative AI, RAG, agentic AI, machine learning, data science, data engineering, and cloud-native application development.

The engineer will own the delivery lifecycle, including requirements analysis, technical design, development, testing, deployment, monitoring, debugging, and continuous improvement. The role involves integrating AI capabilities with enterprise data and applications while ensuring security, scalability, performance, and maintainability.

Key Responsibilities

· Design, develop, deploy, and support production-grade AI and machine learning solutions throughout the complete engineering lifecycle.

· Design and implement production-grade RAG and GraphRAG architectures, selecting vector, hybrid, or knowledge graph-based retrieval patterns based on data, use-case, scalability, and response-quality requirements.

· Design and develop agentic AI solutions using appropriate patterns such as single-agent, multi-agent, supervisor-worker, human-in-the-loop, and event-driven architectures.

· Implement agentic workflows using frameworks such as LangGraph, LangChain, Semantic Kernel, AutoGen, CrewAI, LlamaIndex, or equivalent technologies.

· Develop AI-driven workflow automation by integrating agents with enterprise applications, APIs, tools, business rules, and approval processes.

· Develop and evaluate machine learning and deep learning models, including NLP use cases, using sound data science, experimentation, validation, and performance measurement practices.

· Design data models and build scalable data pipelines for high-volume, diverse datasets across relational, NoSQL, analytical, search, vector, and graph data platforms.

· Build AI-enabled APIs, microservices, and application components and integrate them with enterprise and user-facing systems.

· Deploy and operate AI workloads on AWS, Microsoft Azure, or Google Cloud using established software engineering, MLOps, LLMOps, and CI/CD practices.

· Monitor, troubleshoot, and optimize production AI solutions for quality, performance, scalability, security, governance, and cost.

· Produce maintainable code and technical documentation while contributing to solution design, engineering reviews, production readiness, and stakeholder discussions.

· Engage directly with client and business stakeholders to shape solution design, present architectural trade-offs, and drive consensus on technical direction.

Required Qualifications

Bachelor’s or master’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, Mathematics, Statistics, or a related quantitative discipline.

At least four years of hands-on experience, with demonstrable contributions to three or more production-grade AI projects across design, development, deployment, and support.

Strong hands-on skills in Python, machine learning, deep learning, NLP, and data engineering, including data preparation, modeling, pipeline development, model evaluation, and production-grade coding.

Hands-on data engineering experience building scalable pipelines and data models for high-volume, diverse datasets, including schema design and SQL, NoSQL, analytical, search, or vector data stores.

Experience developing REST APIs, microservices, or back-end services and integrating AI capabilities with enterprise or user-facing applications.

Hands-on experience deploying solutions on at least one major cloud platform: AWS, Microsoft Azure, or Google Cloud.

Experience with Git, CI/CD, automated testing, and containerized or serverless deployments.

Experience monitoring, debugging, and securing production AI solutions using appropriate observability, access control, data privacy, and responsible AI practices.

Excellent verbal and written communication skills, with demonstrated ability to engage with clients/stakeholders

Preferred Qualifications

Relevant professional certifications in AWS, Microsoft Azure, or Google Cloud.

Experience working in a mature product engineering, technology consulting, enterprise data, or AI practice with structured engineering and production-support processes.

Experience with enterprise AI/ML services on at least one major cloud platform; multi-cloud exposure is an advantage.

Experience with modern data platforms, distributed and streaming data processing, workflow orchestration, and messaging systems.

Knowledge of infrastructure as code, Kubernetes, cloud security, observability, and performance engineering.