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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Director of Engineering - Agentic AI platform - **Company:** S S and C LLC - **Location:** Waltham, MA, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** LangGraph Framework, JavaScript (Programming Language), Application Programming Interfaces (APIs), Artificial Intelligence, Airflow, Amazon Web Services, Cloud Computing, Computer Programming, Databases, Continuous Integration, Information Engineering, Extract Transform Load (ETL), DevOps, Github, Graph Database, Identity and Access Management, Python (Programming Language), Machine Learning, Neo4j, NoSQL, Object-Oriented Software Development, Scrum Methodology, Role-Based Access Control, Recommender Systems, OpenAI, Azure Machine Learning, Secure Coding, Software Construction, Software Engineering, SQL Databases, TypeScript, Pinecone, Qdrant, Pytorch, LangChain, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Apache Spark, Google Vertex AI, Model Validation, Llamaindex, Generative AI, AWS Lambda, Agentic-AI, AI Platforms, Kubernetes, Information Technology, HuggingFace, CrewAI, Data Analytics, Performance Monitor, AutoGen, Apache Nifi, Milvus, Ollama, Claude, Semantic Kernel, Serverless Computing, AWS EKS, Docker, Jenkins, Programming Languages, Microservices - **Published:** October 26, 2026 - **Apply:** https://www.adzuna.com/details/5846217753 ## About the Role * Programming & Frameworks: Expert Python skills (OOP, async, testing, packaging) and hands-on experience with agentic libraries including LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, and BeeAI. Familiarity with JavaScript/TypeScript (for edge agents or UI integrations) is a plus. * LLMs & Machine Learning: Strong knowledge of LLMs (GPT-4o, Claude, Llama 3, domain-specific models) and fine-tuning techniques. Understanding of embeddings, vector similarity search (ANN), and RAG pipelines. Experience with providers such as OpenAI, Anthropic, Hugging Face, and Ollama. * Databases & Data Engineering: Proficiency with vector databases (Pinecone, Milvus, Qdrant), graph databases (Neo4j, Amazon Neptune), and traditional SQL/NoSQL systems. Ability to design schemas and queries for agent context management. Experience building ETL pipelines (Airflow, NiFi, Spark) to populate and maintain knowledge bases. * DevOps & Deployment: Skilled in containerization (Docker), orchestration (Kubernetes, AWS EKS/GKE), serverless (AWS Lambda, Cloud Run), CI/CD (GitHub Actions, Jenkins), and infrastructure-as-code. Familiarity with cloud ML platforms (AWS Bedrock, GCP Vertex AI, Azure ML) for scalable agent hosting. * Security & Governance: Knowledge of secure coding practices, IAM/RBAC, data encryption, and AI governance (prompt injection mitigation, bias auditing, compliance documentation). * Soft Skills: Strong problem-solving, communication, and collaboration skills. Ability to translate business requirements into technical specifications. Intellectual curiosity and adaptability to stay current in a fast-moving field. * Master's degree or PhD, or equivalent experience in Data Science, Information Technology, Applied Mathematics, Engineering, Computer Science or related field. * 5 to 7 years of data science and/or software engineering experience in Artificial Intelligence and Machine Learning with a proven track record of building and deploying AI models. * 12+ years of experience leading, managing, and developing highly talented teams. * Strong technical background and deep understanding of Machine Learning with practical experience in building and implementing large-scale predictive models and recommendation systems. * Proficient in AI frameworks (e.g., PyTorch) and programming languages (e.g., Python), with experience in building and deploying AI models for end-to-end AI/ML solutions. * Proven leadership experience in data analytics and AI, or a related role, with a strong background in designing and implementing data science solutions. * Prior experience leading technical engagements across data engineering, data science, AI, and Gen AI workstreams. * Strong leadership and proactive communication to coordinate with the project teams and other internal stakeholders. * Experience with delivering solutions on major cloud platforms, data science tools, and Gen AI technologies. * Strong Fin-Tech business acumen, with experience in developing PoCs and bringing them to product vision. * Utilize expertise to guide the decision on leading-edge technical / business approaches and/or develops major new technical tools. * Facilitate communication between executives, staff, management, vendors, and other technology resources within and outside of the organization. ## Description * Lead and manage multiple engineering teams focused on AI, Data Science, and platform development. * Drive the design and implementation of Agentic AI frameworks, including orchestration, tool use, memory, workflow automation, and multi-agent systems. * Oversee the delivery of AI/ML and LLM-based solutions from concept through production, ensuring scalability, security, and maintainability. * Establish software engineering best practices for AI development, including CI/CD, testing, observability, model lifecycle management, and performance monitoring. * Partner with Product Management to define roadmaps, prioritize use cases, and ensure timely delivery of high-impact AI capabilities. * Collaborate with architecture, cloud, and platform teams to build reusable AI services and enterprise-ready frameworks. * Ensure strong execution discipline across teams, including sprint planning, milestone tracking, and delivery accountability. * Guide teams in building production-ready solutions using modern AI technologies (LLMs, vector databases, RAG, agent frameworks, APIs, microservices, cloud platforms). * Promote responsible AI practices, including governance, data privacy, model evaluation, and risk controls. * Mentor engineering managers, technical leads, and senior engineers to build a high-performing organization. * Drive cross-functional collaboration with Product, UX, Data, Security, and Infrastructure teams. * Communicate progress, risks, and outcomes to senior leadership and stakeholders * Identify opportunities to standardize platforms, reduce duplication, and accelerate delivery through shared frameworks.