Senior AI Engineer

About the role

Newfold Digital is looking for a hands-on Senior Applied AI Engineer who combines strong software engineering fundamentals with practical AI problem solving. The role builds production AI experiences across brands such as Network Solutions, including conversational agents, business and website creation, knowledge and FAQ agents, content experiences, domain discovery and intelligent automation. It works with senior engineers, architects and product partners to turn customer and product problems into reliable, secure, observable and cost-efficient AI systems.

Key responsibilities

• Translate product requirements into practical AI solutions using LLMs, RAG, tool calling, agents, deterministic workflows or traditional software.
• Build production AI services in Python, FastAPI, with asynchronous workers, PostgreSQL, Redis, queues, model APIs and external tools.
• Implement tool calling agents and multi-step workflows that interact with internal APIs, MCP tools, business systems and knowledge sources.
• Build event-driven workflows with RabbitMQ, Kafka, Azure Service Bus or equivalents, including retries, dead letter handling and idempotency.
• Build and improve RAG systems covering ingestion, chunking, embeddings, hybrid retrieval, reranking, metadata filtering, context construction and citations.
• Integrate models from OpenAI, Anthropic, Google, xAI and open-weight ecosystems, choosing them by quality, latency, reliability and cost.
• Build and run AI evaluations using curated datasets, regression tests, retrieval metrics, LLM-as-judge techniques and tool execution evaluation.
• Debug hallucinations, retrieval failures, incorrect tool usage, agent loops, latency issues, provider failures and unexpected inference costs.
• Use AI coding agents such as Cursor, Claude Code or OpenAI Codex to speed up implementation, testing, debugging, refactoring and documentation.

Required profile

• 5+ years of professional software engineering experience building production backend, distributed or cloud-based systems.
• At least 1 year of hands-on experience building applied AI, LLM, RAG, NLP or agent-based applications; production experience strongly preferred.
• Comfortable turning product goals into testable technical tasks, prototyping alternatives and choosing simple, maintainable solutions.
• Nice to have: understanding of prompting, structured outputs, tool schemas, context management, retries, fallbacks, rate limits, caching and streaming responses; ability to use experiments and evaluations to compare approaches; knowledge of retrieval quality, grounding, hallucination mitigation, prompt injection risks and agent guardrails.

Required skills

• Strong Python, including FastAPI, async programming, Pydantic, SQLAlchemy or SQLModel.
• Backend and distributed systems fundamentals: REST APIs, concurrency, background processing, caching, reliability and production debugging.
• RabbitMQ, Kafka, Azure Service Bus or equivalent queue and messaging architectures (required).
• Hands-on experience integrating LLM APIs and building structured output, function calling, tool calling or agent execution workflows.
• Experience with RAG or knowledge-grounded systems, including retrieval, embeddings, indexing, context construction and evaluation.
• PostgreSQL and data modeling; exposure to Redis, pgvector, vector databases or hybrid search.
• Demonstrated use of AI coding agents as part of regular software engineering work.
• Nice to have: Docker, CI/CD, automated testing, logging, observability, distributed tracing, OpenTelemetry, Langfuse, Semantic Kernel, Microsoft Agent Framework, LangGraph, PydanticAI, Model Context Protocol, LiteLLM, Azure AI Search, Chroma, Kubernetes, Azure, OCI.