Senior Applied AI Engineer

Build and operationalize end-to-end agentic AI solutions, like RAG pipelines, multi-agent workflows, and human-in-the-loop steps, for enterprise clients

Translate solution designs and business requirements into clean, well-tested production-grade code aligned with phData methodologies, standards, and best practices.

Build production-grade agents emphasizing reliability, scalability, security, observability, and evaluation and guardrail strategies for safe, accurate performance before and after deployment.

Ensure engagements are delivered on time, within scope, and with measurable business value for clients.

Contribute hands-on to reusable agentic accelerators and reference implementations aligned with the Intelligence Platform that can be deployed across multiple clients.

Help establish engineering patterns, tooling, and ways of working as part of the zero-to-one buildout of the India delivery center.

Collaborate with cross-functional partners, including forward-deployed engineers, data engineers, platform/DevOps engineers, and business stakeholders to deliver successful client engagements.

Ensure high-quality deliverables through code reviews, technical documentation, automated testing, and adherence to security and governance standards.

Partner with practice and account leaders to identify opportunities to expand engagements, improve delivery patterns, and standardize AI deployment approaches.

Contribute to internal initiatives such as IP development, accelerators, reference architectures, templates, playbooks, and training for colleagues on best practices for AI engineering.

Represent phData with professionalism in all interactions, communicating clearly with both technical and non-technical stakeholders.

You are a hands-on AI learning engineer and collaborative problem solver who enjoys solving ambiguous business problems with AI, creating robust, production-ready solutions. You are comfortable working across the stack—from data pipelines and infrastructure to APIs and monitoring—and partnering directly with forward-deployed engineers, data engineers, platform engineers, and business stakeholders. You thrive in an outcomes-driven environment and want to help build something from the ground up.

Required Qualifications

4+ years of experience in AI/ML engineering, software engineering, or data engineering roles building and deploying AI/ML solutions to production.

Recent hands-on experience with LLM-powered or agentic applications

Technical / Functional Skills

Hands-on understanding of Large Language Models, agentic architectures, and retrieval frameworks (RAG), including prompt design, context engineering, and tool/function calling

Experience with LLM orchestration frameworks (e.g. LangChain, LlamaIndex) and the broader agentic stack, such as multi-agent patterns, memory, planning, and protocols (e.g. MCP)

Experience designing evaluation, guardrail, and observability strategics for LLM and agent systems (LLMOps/AgentOps)

Hands-on experience in modern programming languages, such as Python, including developing APIs

Deep expertise in cloud-native AI/ML platforms (e.g. AWS Bedrock and SageMaker, Azure AI/ML, Snowflake Cortex) with proven experience deploying solutions into production

Complete software development lifecycle experience including design, documentation, implementation, testing, deployment, and ongoing operations

Demonstrated use of AI-assisted development tooling to accelerate delivery while maintaining quality

Strong working knowledge of SQL, including writing, debugging, and optimizing complex and distributed queries.

Demonstrated ability to work effectively with distributed and cross-functional teams across the US, LATAM, and India.

Proven track record of taking ownership, managing multiple priorities, and delivering high-quality work with minimal supervision.

Comfort working in client environments, quickly understanding new systems, and adapting solutions to fit existing architectures and processes.

Bachelor’s degree in Computer Engineering, Computer Science, or a related technical field, or equivalent practical experience preferred

Impactful Work: Partner with leading organizations on meaningful data & AI initiatives.

Collaborative Culture: Work with a supportive, high-performing global team that values transparency, autonomy, and continuous improvement.

Growth Opportunities: Access to challenging projects, mentorship, and structured development pathways.

Values-Driven: We prioritize doing the right thing for our clients, our teams, and our community.