Engineering Specialist – Generative AI & Machine Learning

GTT•Princeton, NJ

20h•Hybrid

About The Position

Our client, a global management consulting and technology organization, is seeking an experienced Engineering Specialist – Generative AI & Machine Learning to join a highly advanced AI and engineering practice focused on delivering scalable, production-grade AI solutions. This role is ideal for a hands-on technical leader who can combine deep expertise in Generative AI, machine learning, cloud engineering and scalable software architecture with strong client-facing and team leadership capabilities. The successful candidate will help design, develop and deploy modern AI solutions, including RAG pipelines, agentic AI workflows, LLM-powered applications, responsible AI controls and enterprise-grade AI platforms. This individual will also work closely with technical architects, engineering teams and client stakeholders to drive technical decision-making and successful delivery.

Requirements

Strong hands-on programming experience with Python and JavaScript.
Strong experience developing APIs using frameworks such as FastAPI, Django or comparable technologies.
Strong experience designing and delivering Generative AI and LLM-based applications.
Hands-on experience with: RAG architectures, Vector databases, Vector and hybrid search, AI agents and agentic workflows, Prompt engineering, LLM orchestration frameworks.
Experience integrating with major LLM platforms and APIs such as OpenAI, Anthropic and AWS Bedrock.
Experience with AI/LLM development frameworks such as LangChain, DSPy or similar technologies.
Strong cloud experience across AWS and/or Azure.
Hands-on experience with Databricks, including Unity Catalog.
Strong Infrastructure-as-Code experience using Terraform and/or CloudFormation.
Hands-on experience with Docker and Kubernetes.
Experience with AWS services such as Redshift, RDS, S3 and related cloud infrastructure services.
Experience designing and operating production systems supporting large user populations.
Experience with scalable application technologies such as Redis, vector search and distributed systems.
Strong understanding of scalable application architecture, security best practices, privacy requirements and enterprise-grade software development.
Experience with version control using Git and modern DevOps/CI/CD practices.
Experience with Agile/Scrum development methodologies.
Strong understanding of software development lifecycle practices and production engineering standards.
Excellent communication skills with the ability to explain complex technical concepts to technical and non-technical audiences.
Demonstrated ability to work effectively in a client-facing environment.
Strong ownership, collaboration and problem-solving capabilities.
Bachelor's or Master's degree in Computer Science, Information Technology, Engineering or a related discipline, or equivalent professional experience.
6 years of relevant experience in software engineering, AI/ML engineering, data engineering, cloud engineering or related technology roles.

Nice To Haves

Experience with front-end technologies including React.js, Next.js and Tailwind CSS.
Experience designing enterprise AI governance and responsible AI frameworks.
Experience with Azure DevOps.
Experience building or supporting AI applications at significant production scale.
Previous experience working within management consulting, technology consulting or other professional services environments.
Experience delivering AI, analytics or technology solutions within healthcare, pharmaceutical, biotechnology, life sciences or other highly regulated industries.

Responsibilities

Lead technical execution across complex AI, machine learning and software engineering initiatives.
Oversee task planning, workload distribution and technical delivery across project teams.
Serve as a trusted technical advisor to clients, translating complex AI/ML concepts into practical recommendations and business outcomes.
Manage client expectations throughout the project lifecycle.
Lead, mentor and provide technical guidance to junior engineers and team members.
Facilitate technical discussions with internal teams and client stakeholders to support decision-making and resolve delivery challenges.
Partner with technical architects to validate solution designs and implementation approaches.
Design and develop scalable, distributed and production-ready software architectures.
Own technical execution, including code quality, delivery timelines, technical standards and effective allocation of engineering resources.
Apply data-driven decision-making to support product and project objectives.
Design, develop and deploy LLM-based pipelines using patterns such as: Retrieval-Augmented Generation (RAG), Agentic workflows, AI agents, Prompt engineering, Parameter-Efficient Fine-Tuning, including LoRA and QLoRA.
Manage the complete software development lifecycle, including requirements analysis, architecture, development, testing, deployment and ongoing support.
Design and deploy solutions using AWS, Azure and Databricks.
Leverage cloud capabilities including identity and access management, monitoring, networking, load balancing, autoscaling, databases, container registries and storage.
Design governance frameworks using Databricks Unity Catalog, supporting governance from the underlying data layer through AI model outputs.
Develop and implement prompt and response guardrails to support responsible AI, privacy, security and governance requirements.
Develop Infrastructure-as-Code solutions using Terraform and CloudFormation.
Implement DevOps practices using Docker, Kubernetes and CI/CD technologies.
Develop automation and monitoring scripts to improve deployment efficiency, reliability and observability.
Conduct code reviews and establish software engineering and development best practices.
Collaborate across engineering, data, product, architecture and consulting teams to deliver high-quality client solutions.