Associate Director, Full-stack Forward Deployed Engineer

Kyndryl•Dallas, TX

1d•$143,640 - $273,000•Hybrid

About The Position

Kyndryl Consult is driving the next wave of enterprise AI transformation, delivering mission-critical innovation for global organizations. Operating at the intersection of deep technical architecture and client co-creation, our teams move at market speed to turn complex business challenges into production-ready software systems. As an Associate Director, Full-stack Forward Deployed Engineer, you are the principal technical force customers ask for by name. You will lead high-impact AI engagements, shape modern architectural blueprints, and step into an ultra-fast, high-autonomy builder’s environment. In this role, you take direct accountability for real outcomes in production environments, designing responsive modern frontends, building robust backend data pipelines, and orchestrating advanced multi-agent workflows. Operating from our physical Agentic AI Lab in Frisco, Texas, and working directly alongside enterprise leadership, you will thrive in an iterative, rapid-prototyping ecosystem where we ship functional MVPs in weeks, not months. You will balance hands-on development—utilizing state-of-the-art retrieval-augmented generation (RAG) architectures, managing LLM API performance, and deploying to cloud hyper-scalers—with strategic client advisory to ensure every solution delivers measurable business value.

Requirements

Based in the Dallas-Fort Worth metroplex (or ready to relocate immediately) with a hybrid schedule of 3 days onsite per week at the Frisco, TX Agentic AI Lab.
4+ years of professional software engineering experience with strong proficiency in Python and JavaScript/TypeScript backend development.
Hands-on experience building, designing, and deploying applications using modern frameworks and orchestrators (e.g., LangChain, AutoGen, or CrewAI) in production.
Experience designing and maintaining modern frontend applications (React, Angular, or Vue), particularly handling streaming data or real-time query responses.
Practical understanding and deployment experience with Retrieval-Augmented Generation (RAG) pipelines and vector databases (e.g., Pinecone, Milvus, Qdrant, Chroma).
Experience deploying containerized applications using Docker and Kubernetes to major cloud hyper-scalers (AWS, Azure, or GCP) and distributed computing architectures.
Strong proficiency with SQL (PostgreSQL) and NoSQL databases, alongside solid data engineering tools such as Pandas and Spark.
Deep familiarity with Git, GitHub, CI/CD pipelines, version control, and modern software delivery best practices.

Nice To Haves

Experience with LLM infrastructure tools such as LiteLLM gateways, prompt tracing platforms (e.g., Langfuse), or workflow managers (e.g., Temporal).
Familiarity with open-source AI ecosystems (e.g., Hugging Face), agentic frameworks (e.g., LangGraph, Semantic Kernel), system prompt tuning, model fine-tuning, and vector search caching strategies (e.g., Redis).
Experience using messaging queues or streaming platforms like Kafka or RabbitMQ, alongside familiarity with AI/ML frameworks like TensorFlow or PyTorch.
Strong business acumen with the ability to translate complex business requirements into scalable AI solutions and communicate value to executive stakeholders.
Willingness to travel and work on customer premises as required.
Active professional certifications in major cloud hyper-scalers (AWS, Azure, GCP) or AI-specific development pathways.
Bachelor's degree or higher in Computer Science, Software Engineering, Data Science, Informatics, or a related technical field (or equivalent practical work experience).

Responsibilities

Build and scale responsive modern frontend interfaces using React, Angular, or Vue designed to handle dynamic generative UI elements and real-time streaming outputs.
Develop robust backend APIs, microservices, and data pipelines using Python, JavaScript/TypeScript, or Node.js to connect enterprise systems with advanced language models.
Rapidly prototype, iterate, and ship functional, production-ready MVPs within weeks in a highly agile lab environment.
Optimize application performance, query execution, and rendering speeds to maintain high reliability and low latency across distributed systems.
Design, build, and deploy multi-agent orchestration frameworks using tools like LangChain, AutoGen, or CrewAI that communicate, self-correct, and execute complex enterprise workflows.
Configure and optimize Retrieval-Augmented Generation (RAG) architectures and vector engines, integrating both structured and unstructured data sources.
Resolve LLM deployment challenges, including API call optimization, token latencies, prompt routing, context window limits, and cost management.
Implement system-level security and performance best practices for scalable AI platforms.
Embed directly with enterprise client teams to translate ambiguous business requirements into scalable, high-impact technical architectures.
Partner with senior stakeholders to prioritize long-term technical value and solution integrity over quick fixes.
Own end-to-end technical delivery from initial scoping through deployment, driving user adoption and operational outcomes.
Systematically capture deployment learnings and document architectural best practices to inform ongoing enhancements to Kyndryl’s core platforms.
Collaborate daily with lab engineering leads to refine physical deployment pipelines and shared frameworks.
Share knowledge, code contributions, and insights openly across Slack, Teams, and cross-functional stand-ups to elevate the broader engineering community.