Note: The job is a remote job and is open to candidates in USA. Signify Technology is seeking a Senior Applied AI Engineer to design, build, and deploy production-grade AI products and intelligent workflows. The role focuses on translating generative AI capabilities into secure, scalable, observable systems, including LLM applications, AI agents, RAG pipelines, and Python services while partnering with cross-functional stakeholders and mentoring engineers.
Responsibilities
- Design and build LLM-powered applications, AI agents and workflow-automation solutions
- Develop agentic systems capable of reasoning, retrieving context, calling tools and completing multi-step tasks
- Build and optimise Retrieval-Augmented Generation pipelines, including ingestion, chunking, embeddings, retrieval and reranking
- Integrate models from providers such as OpenAI, Anthropic, Google and AWS
- Evaluate different models and architectures based on accuracy, latency, reliability and cost
- Develop evaluation frameworks to measure model and agent performance
- Implement guardrails, structured outputs, fallback handling and human-in-the-loop processes
- Build production-quality Python services and APIs around AI functionality
- Deploy and operate AI applications within cloud environments
- Monitor quality, hallucinations, latency, token usage, model drift and production failures
- Partner with product managers, designers, data teams and business stakeholders
- Translate ambiguous business requirements into practical technical solutions
- Contribute to architectural decisions and establish AI-engineering standards
- Mentor engineers and provide technical guidance across AI initiatives
Skills
- Five or more years of professional software-engineering experience
- Commercial experience building and deploying AI or machine-learning products
- At least one to three years of hands-on generative AI or LLM experience
- Strong proficiency in Python and modern backend development
- Experience building production applications using large language models
- Practical knowledge of prompt and context engineering
- Experience with agentic workflows, function calling and tool use
- Strong understanding of RAG, embeddings, semantic search and vector databases
- Experience with frameworks such as LangGraph, LangChain, LlamaIndex or equivalent
- Experience using OpenAI, Anthropic, Gemini, Bedrock or open-source models
- Experience creating evaluation, testing and monitoring processes for LLM applications
- Familiarity with AWS, Azure or Google Cloud
- Experience with APIs, Docker, CI/CD and distributed systems
- Strong communication and cross-functional problem-solving skills
- Evidence of independently delivering products from concept to production
- Kubernetes and production infrastructure
- AWS Bedrock, SageMaker, Azure OpenAI or Vertex AI
- Pinecone, Weaviate, pgvector, Elasticsearch or OpenSearch
- LangSmith, Langfuse, MLflow or similar observability tools
- Model fine-tuning and open-source model deployment
- Multi-agent architectures and Model Context Protocol integrations
- AI security, governance and responsible-AI practices
- Experience working with sensitive or regulated data
- Previous start-up or customer-facing engineering experience
Benefits
- Remote/hybrid work arrangement
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