Develop scalable AI engineering patterns, tool-use approaches, and human-in-the-loop controls.
Apply architecture decisions that balance quality, safety, latency, cost, and model risk.
Deliver production-quality code using strong practices in testing, CI/CD, logging, versioning, and documentation.
Design extensible functionality, support sprint sizing, and align solutions with senior team members.
Contribute reusable assets including code, prompt libraries, runbooks, and reference implementations.
The team
AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements.
Required qualifications
Bachelor's degree (or equivalent) in Computer Science, Data Science or Engineering.
3+ years of experience in software engineering, data engineering, data science, or analytics engineering.
1+ years of hands-on experience building and deploying GenAI/LLM-powered solutions in client or production environments
1+ years of experience with Palantir including hands-on experience with one of the following key platforms/products; Foundry, AIP, Maven
1+ years of experience leading project workstreams/engagements and translating business problems into AI solutions
1+ years of experience building reliable, maintainable, and well-documented code
Ability to travel 50%, on average, based on the work you do and the clients and industries/sectors you serve
Limited immigration sponsorship may be available
Preferred qualifications
Experience with cloud environments (AWS, Azure, and/or Google Cloud) and common platform services (storage, compute, IAM, networking)
Demonstrated ability to work directly alongside client technical teams and program stakeholders in fast-paced, ambiguous delivery environments
Data engineering experience with Spark, Airflow/dbt, streaming, data modeling or ML/data science background feature engineering, experimentation or model evaluation
Experience with MLOps/LLMOps practices: evaluation frameworks, model monitoring, and prompt management
Experience integrating LLM solutions with enterprise systems via APIs, microservices, or event-driven architectures
Experience operating within hybrid onshore/offshore teams
Familiarity with security, privacy, and compliance considerations
The wage range for this role takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs. The disclosed range estimate has not been adjusted for the applicable geographic differential associated with the location at which the position may be filled. At Deloitte, it is not typical for an individual to be hired at or near the top of the range for their role and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is $134,500-$265,100.
You may also be eligible to participate in a discretionary annual incentive program, subject to the rules governing the program, whereby an award, if any, depends on various factors, including, without limitation, individual and organizational performance.
