Company Overview 10Pearls is an award-winning end-to-end digital innovation company that helps businesses imagine and build the future. We are proud to announce that 10Pearls was named as winner of the Best Tech Work Culture Timmy Award in Washington DC by Tech in Motion, recognized on the Inc. 5000 Fastest-Growing Companies List, and was ranked the #1 Most Diverse Midsize Company in Greater Washington. We partner with businesses to help them transform, scale, and accelerate by adopting digital and exponential technologies. Our work has ranged from creating highly usable, secure digital experiences, mobile and software products, to helping businesses modernize through cloud adoption and development and the digitalization of their business processes. Our clientele is highly diverse, including Global 1000 enterprises, mid-market businesses, and even high-growth start-ups. But those are just facts. What makes us unique is that we have a true heart and soul. We have a strong focus on a double bottom line and actively support and engage with the communities where we live and work to make the world a better place. In a nutshell, we believe in doing well, while doing good and know how to balance the two. Role: As an ML Team Lead, you will be responsible for leading the technical direction of AI/ML initiatives while remaining deeply hands-on in building scalable production-grade ML and LLM systems. This is a highly technical leadership role where approximately 70% of the time will be spent designing systems, writing production code, conducting design and code reviews, debugging production issues, and shipping ML/AI solutions, while 30% will focus on technical leadership, mentoring engineers, and collaborating with product and engineering leadership on roadmap planning. You will play a critical role in architecting and scaling AI/ML systems including classical machine learning models, Retrieval-Augmented Generation (RAG) pipelines, multi-step agentic systems, and LLM-powered applications. You will also establish engineering standards around scalability, security, resilience, observability, cost optimization, and production readiness for all AI systems built by the team. Responsibilities: - Lead the technical direction for the team’s ML and LLM systems, including architecture patterns, platform choices, evaluation frameworks, and engineering standards - Stay hands-on by designing and implementing complex ML and agentic AI systems, writing production-grade code, and leading through technical execution - Design, develop, and deploy scalable ML and LLM-powered applications and services in production environments - Build and optimize AI-powered solutions such as RAG systems, multi-step agents, AI assistants, chatbots, forecasting systems, ranking models, classification models, and optimization systems - Drive architecture and design reviews to ensure scalability, reliability, security, and maintainability of AI/ML systems - Own the technical roadmap for ML/LLM initiatives and translate business objectives into execution plans and scalable solutions - Collaborate closely with Product Managers, Engineers, Data Engineers, MLOps Engineers, QA Engineers, and cross-functional stakeholders to deliver business-aligned AI solutions - Establish engineering best practices for prompt engineering, model evaluation, regression testing, observability, and production readiness - Define and implement quality standards, evaluation suites, acceptance metrics, and regression plans for all AI/ML features - Ensure high availability, scalability, and resilience of tier-1 ML services through SLOs, monitoring, incident response, failover strategies, circuit breakers, and multi-zone deployments - Drive security and safety best practices for AI systems, including handling of untrusted inputs, prompt injection prevention, secure tool usage, PII protection, and internal security reviews - Optimize infrastructure, token, GPU, and operational costs by making cost efficiency a first-class design consideration - Mentor and grow ML, GenAI, Data, MLOps, and QA engineers through technical guidance, design discussions, code reviews, pairing sessions, and growth planning - Partner with the MLOps team on platform strategy and collaborate with Data Engineering teams to build scalable data foundations for ML systems - Participate in architecture councils, product reviews, engineering planning sessions, and security reviews as the representative of the engineering team - Support hiring efforts by interviewing, calibrating, onboarding, and mentoring new engineers - Stay updated with emerging trends and advancements in AI/ML, LLMs, cloud platforms, MLOps, and agentic AI frameworks Requirements: - Bachelor's or Master's degree in computer science, Artificial Intelligence, Data Science, Software Engineering, or a related field - 7+ years of professional software engineering experience with at least 5 years of hands-on experience building and deploying ML systems into production - Prior experience as a Tech Lead, Staff Engineer, or hands-on lead for AI/ML engineering teams - Strong expertise in classical machine learning domains such as forecasting, ranking, classification, and optimization - Hands-on experience building modern LLM and agentic AI systems including RAG pipelines, tool-using agents, multi-step workflows, and evaluation systems - Strong proficiency in Python and backend system development - Experience with ML frameworks such as PyTorch or TensorFlow - Strong understanding of scalable distributed systems, APIs, system integration, architecture design, and production engineering practices - Experience operating ML services at scale, including SLO management, monitoring, on-call practices, and incident response - Experience working with Kubernetes-based deployments, CI/CD pipelines, and modern cloud-native engineering practices - Production experience with cloud platforms such as Azure, AWS, or GCP, preferably Azure and AKS - Hands-on experience with vector databases, semantic search systems, and LLM evaluation methodologies - Experience with prompt engineering, model evaluation, regression testing, and AI system optimization - Strong understanding of system reliability, resiliency patterns, observability, scalability, and performance optimization - Experience working with modern data warehouses and distributed data systems - Strong leadership, mentoring, stakeholder management, analytical, and problem-solving skills - Excellent written and verbal communication skills in English - Ability to collaborate effectively with technical and non-technical stakeholders across distributed teams Nice to Have: - Experience in supply chain, logistics, warehousing, or e-commerce domains - Experience scaling AI/ML engineering teams and mentoring engineers across multiple disciplines - Experience with Azure AI Foundry, AWS Bedrock / AgentCore, Amazon SageMaker, or Vertex AI - Experience driving security and compliance reviews for AI/ML systems - Open-source contributions, conference speaking engagements, or published technical writing