Senior Director, Engineering – Data, ML & AI

What you'll do

Define and drive the multi-year roadmap for Data, ML, and AI engineering aligned with business priorities.
Champion the adoption of the AI Development Life Cycle (AIDLC) – from ideation, data readiness, experimentation, evaluation, deployment, to continuous monitoring and improvement.
Evangelize an AI-first engineering culture across the organization, balancing speed with rigor, safety, and responsible AI principles.
Stay deeply technical – review architectures, contribute to design decisions, pair with engineers, and unblock teams by rolling up your sleeves when needed.
Architect and deliver scalable, performant, and cost-efficient data platforms (batch, streaming, lakehouse, feature stores, and real-time analytics).
Lead the build-out of AI/ML platforms and GenAI solutions including model training pipelines, inference infrastructure, RAG systems, agentic workflows, evaluation frameworks, and MLOps tooling.
Ensure engineering excellence through strong standards around reliability, observability, security, data quality, and performance.
Build, mentor, and scale high-performing engineering teams across Data Engineering, ML Engineering, AI/Platform Engineering, and Analytics Engineering.
Enable teams to work in an AIDLC model – equip them with the right tools, guardrails, playbooks, and AI-assisted developer workflows to accelerate delivery.
Foster a culture of experimentation, rapid iteration, data-driven decision making, and customer obsession.
Grow leaders – hire, coach, and develop engineering managers and senior technical talent.
Partner closely with Product, Data Science, Security, and Business stakeholders to translate ambiguous problems into concrete deliverables.
Drive delivery in a fast-paced environment with aggressive timelines, making pragmatic trade-offs between speed, quality, and scalability.
Own budgets, vendor relationships, and technology choices for the data and AI stack.
Establish metrics and operating rhythms to measure platform health, team velocity, and business impact.

What We’re Looking For

15+ years of software engineering experience, with 7+ years in engineering leadership roles managing managers and senior engineers.
Proven track record of building and scaling large, performant data platforms handling petabyte-scale workloads (e.g., Spark, Kafka, Flink, Snowflake, Databricks, BigQuery, Iceberg/Delta/Hudi).
Deep, hands-on experience designing and delivering AI/ML solutions and platforms – including classical ML, deep learning, and modern GenAI / LLM applications (RAG, fine-tuning, agents, evaluation, guardrails).
Strong background in MLOps / LLMOps – model lifecycle management, CI/CD for ML, feature stores, vector databases, and production inference at scale.
Demonstrated experience operating in and enabling teams with AI-driven development practices (AIDLC) – leveraging AI tooling to accelerate coding, testing, data pipelines, and engineering workflows.
Cloud-native expertise on AWS, GCP, or Azure, with strong grounding in distributed systems, microservices, Kubernetes, and infrastructure-as-code.
Track record of thriving in fast-paced, high-growth, ambiguous environments – shipping iteratively while maintaining engineering quality.
Player-coach mindset – equally comfortable in code reviews, architecture whiteboards, and executive steering committees.
Exceptional communication skills – able to translate complex technical concepts for executives, customers, and cross-functional partners.
Strong bias for action, ownership, and outcomes over outputs.
Empathetic and inclusive leader who builds trust and psychological safety on their teams.
Strategic thinker who can zoom out to set direction and zoom in to debug a pipeline.
Applicants must be legally authorized to work in the United States without the need for employer sponsorship, now or in the future. PDI Technologies is unable to offer visa sponsorship for this role.

Nice to Have

Experience building developer platforms or internal AI copilots that multiply engineering productivity.
Contributions to open source in the data or ML ecosystem.
Experience in regulated industries with strong data governance and privacy requirements.
Advanced degree in Computer Science, Engineering, Mathematics, or a related field.