Job Description
EXECUTIVE PRACTICE LEADER – AI DATA CENTERSWHO WE ARE Apex Systems is a leading global technology services business that incorporates industry insights and experience to deliver solutions that fulfill our clients’ digital visions. We provide a continuum of services, including strategy and enablement, innovation and productivity, and technology foundations to drive better results and bring more value to our clients. Apex transforms our customers with modern enterprise solutions tailored to the industries we serve. Apex has a presence in over 70 markets across North America, Europe, and India. Apex is a part of the Commercial Segment of ASGN Incorporated (NYSE: ASGN). To learn more, visit www.apexsystems.com. At Apex Systems, we prioritize professional development, work-life balance, and fostering a collaborative culture. We value our team's well-being and recognize the importance of building strong relationships. That's why we organize regular team-building events and philanthropic days to give back to the community - fostering a sense of purpose and fulfillment among our team. Join us for career advancement, innovative solutions, and a supportive environment focused on your success.JOB DESCRIPTIONApex Systems is seeking a Senior Lead Consultant to serve as an ML & AI Engineering Lead within our Solutions Delivery Organization. This role blends hands-on technical delivery with solution architecture and technical leadership, supporting clients as they design, build, and scale AI-driven applications.The ideal candidate is equally comfortable designing end-to-end AI solutions and writing production-grade code. You will lead the development of machine learning and generative AI solutions on AWS, guide architectural decisions, mentor engineers, and partner with clients to deliver scalable, secure, and production-ready AI systems. Experience with digital twin, telemetry, or IoT-driven use cases is a strong plus. Employees who are not actively deployed on a client project and reside within a one-hour commute of an Apex branch office are expected to work onsite at least once per week. RESPONSIBLITIESDesign end-to-end applied AI and machine learning solutions spanning data ingestion, model development, orchestration, deployment, and monitoring.Define architectural standards and best practices for ML and generative AI systems, ensuring scalability, reliability, security, and compliance.Perform technical assessments of client environments and develop pragmatic solution architectures and roadmaps.Partner with stakeholders to translate business and operational needs into technical designs and implementation plans.Design, build, and deploy machine learning and generative AI solutions, including chat, summarization, extraction, classification, forecasting, and decision-support use cases.Implement LLM-powered applications using RAG, function/tool calling, agents, and orchestration patterns.Develop scalable ML pipelines for data ingestion, model training, inference, and monitoring using cloud-native services and modern MLOps practices.Develop and operationalize models using Amazon SageMaker, including training, tuning, deployment, and monitoring.Build generative AI solutions using Amazon Bedrock and related AWS services.Develop APIs and services (i.e. FastAPI) to integrate AI capabilities into enterprise applications.Implement evaluation, testing, monitoring, and retraining strategies for AI systems.Lead and mentor junior and mid-level AI engineers across ML, GenAI, and MLOps practices.Guide teams through experimentation, model evaluation, responsible AI practices, and production readiness.Serve as a trusted technical advisor to clients, clearly explaining trade-offs, risks, and architectural decisions.Collaborate with product, design, platform, and data teams to deliver cohesive solutions.Drive project execution, ensuring delivery against scope, timeline, budget, and quality expectations.Identify opportunities for innovation and expanded value delivery across client engagements.JOB REQUIREMENTS8+ years of experience in AI and ML engineering, with 3+ years focused on generative AI or LLM-based solutions.Proven experience designing and delivering production AI solutions using frameworks such as LangChain, Semantic Kernel, or custom orchestration frameworks.Proven experience designing, building and deploying machine learning models and end-to-end ML pipelines including data preparation, feature engineering, model training, evaluation, and inference.Strong hands-on experience with AWS, including, SageMaker, Bedrock, and services such as Textract, Comprehend, Kendra, Polly, IAM, monitoring, and security services.Advanced Python skills, including API development, data processing, and integration with external systems.Experienced in common ML frameworks and libraries (i.e. PyTorch, TensorFlow).Hands-on experience with RAG pipelines, vector databases, and memory architectures (i.e. FAISS, OpenSearch, Pinecone).Strong understanding
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