AI Tech Lead

HMG America LLC is the best Business Solutions focused Information Technology Company with IT consulting and services, software and web development, staff augmentation and other professional services. One of our direct clients is looking for AI Tech Lead in McLean, VA. Below is the detailed job description.

Title: AI Tech Lead

Location McLean, VA

Duration : Long Term contract

About the Role: We are looking for a seasoned AI Tech Lead with deep hands-on experience in building and leading advanced Generative AI solutions. You will spearhead the design and development of innovative Gen AI Agents, Agentic Workflows, and AI-powered applications that address complex challenges in the multi-family business sector. Your expertise in Retrieval-Augmented Generation (RAG), vector databases, and Python frameworks such as LangChain will be critical in delivering scalable, production-grade AI systems. In this role, you will collaborate closely with Gen AI experts, product managers, and data engineers, driving the creation of reliable and scalable AI-driven products that have a tangible business impact.

Responsibilities:
Architect and develop scalable full-stack Gen AI applications, including Agents and Agentic Workflows tailored to diverse business needs.
Build and optimize feature engineering pipelines to extract meaningful insights from both structured and unstructured data, enhancing LLM-based product capabilities.
Ensure data quality and readiness by implementing robust data cleansing and transformation processes for AI model consumption.
Create feedback loops where AI outputs refine and enhance data sources, fostering continuous product improvement.
Develop Python-based microservices for seamless orchestration and integration with Large Language Models (LLMs) and other AI components.
Integrate and deploy machine learning models, including LLMs, RAG, and multi-modal AI, within cloud-native architectures.
Design and maintain RESTful APIs to facilitate communication between system modules.
Lead DevOps efforts by establishing CI/CD pipelines that support efficient and scalable deployment of Gen AI applications.
Collaborate across multidisciplinary teams to deliver end-to-end Gen AI solutions.
Stay abreast of advancements in LLMs, vector search technologies, prompt engineering, and retrieval methods to continuously elevate system performance.

Success Factors:
Robustness: Deliver high-availability, fault-tolerant RAG pipelines and AI systems.
Technical Mastery: Demonstrate deep Python expertise for microservices and AI integration.
Data Excellence: Maintain high standards for data quality and preparation, enabling superior model performance.
Operational Reliability: Implement strong monitoring and observability practices for minimal downtime.
Collaborative Leadership: Communicate effectively and foster teamwork across AI experts, product teams, and engineers.
Innovation Mindset: Keep pace with AI industry trends and proactively incorporate best practices.
Agility: Adapt swiftly in a dynamic environment to continuously enhance AI-driven business solutions.

Core Skills:
Bachelor's degree in Computer Science, Computer Engineering, IT, or related field; advanced degrees preferred.
8-10 years of software engineering experience, with at least 5 years focused on applied AI/ML development.
Proficiency in Python and experience with LangChain or comparable frameworks.
Strong background in data processing, including transformation, cleansing, and feature engineering for AI/ML.
Experience designing APIs, microservices, and distributed systems, particularly within AWS cloud environments.
Solid understanding of MLOps/DataOps pipelines to support scalable AI/ML workflows.
Expertise in logging, tracing, and observability frameworks to ensure system reliability.
Hands-on experience with LLM fine-tuning, prompt engineering, and model evaluation.
Familiarity with managed LLM platforms such as AWS Bedrock is a plus.
Proven ability to work effectively in agile, cross-functional teams, collaborating closely with data engineers to ensure data quality and readiness.