The Role
Design, build, evaluate, and deploy production-grade generative AI, RAG, and agentic systems. Develop LLM applications, multi-agent workflows, backend APIs, evaluation frameworks, guardrails, and AI knowledge systems using Python, FastAPI, vector databases, cloud platforms, and containerization. Collaborate with architects and engineering teams to optimize performance, reliability, scalability, security, and cost while supporting production systems and exploring emerging AI technologies.
Summary Generated by Built In
Role Overview
We are looking for a Senior AI/ML Engineer — Generative AI & Agentic Systems to design, develop, and productionize enterprise-grade Generative AI, RAG, and Agentic AI solutions.
The candidate will work closely with the AI Architect / Technical Lead to translate business requirements into scalable AI solutions and will be responsible for hands-on implementation, integration, evaluation, optimization, and deployment.
This role is ideal for an engineer who enjoys building real-world AI applications using LLMs, RAG pipelines, AI Agents, tool calling, vector databases, orchestration frameworks, and cloud technologies.
Key Responsibilities- Design and develop production-ready Generative AI and LLM-based applications.
- Build scalable Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector databases, reranking, retrieval, and contextual generation.
- Develop Agentic AI workflows using frameworks such as LangGraph and LangChain.
- Implement multi-agent workflows involving planning, reasoning, tool calling, delegation, memory, and orchestration.
- Integrate LLMs from OpenAI, Anthropic, Google Gemini, and other providers.
- Develop backend services and AI APIs using Python, FastAPI, and REST APIs.
- Design and implement LLM evaluation frameworks to measure accuracy, relevance, groundedness, hallucination, latency, and other quality metrics.
- Implement AI guardrails, validation, safety controls, structured outputs, and responsible AI practices.
- Work with PostgreSQL, vector databases, and graph databases to build AI knowledge systems.
- Containerize and deploy AI applications using Docker and cloud platforms such as Azure/AWS.
- Optimize LLM applications for performance, scalability, reliability, and cost.
- Collaborate with architects, frontend/backend engineers, DevOps, and business stakeholders.
- Participate in technical design, code reviews, troubleshooting, and production support.
- Stay current with emerging developments in LLMs, Agentic AI, RAG, open-source models, and AI infrastructure.
- Strong hands-on experience with Python
- FastAPI
- REST API development
- Backend application architecture and development
- LLM application development
- RAG architecture and implementation
- Prompt engineering
- Embeddings and semantic search
- Vector databases
- LLM integration and orchestration
- Experience with one or more of:
- OpenAI
- Anthropic
- Google Gemini
- Hands-on experience building AI Agents
- LangGraph and/or LangChain
- Tool/function calling
- Agent workflows and orchestration
- Multi-agent systems
- Agent state and memory management
- PostgreSQL
- Vector databases
- Docker
- Azure and/or AWS
- API integration and microservices
- LLM evaluation
- RAG evaluation
- Hallucination detection/mitigation
- AI guardrails
- Structured output and schema validation
- Understanding of AI security and responsible AI practices
- Neo4j / GraphRAG
- Model Context Protocol (MCP)
- vLLM
- Ollama
- Open-source LLMs such as Llama, Qwen, Mistral, etc.
- LoRA / parameter-efficient fine-tuning
- Model fine-tuning
- Kubernetes
- CI/CD and Azure DevOps/GitHub Actions
- React / Next.js
- Redis or other caching technologies
- Experience with cloud-based AI/ML infrastructure
- Experience optimizing inference performance and LLM costs
- Strong problem-solving and analytical skills.
- Ability to convert business requirements into practical AI solutions.
- Strong understanding of LLM architecture and modern GenAI patterns.
- Ability to write clean, maintainable, production-quality code.
- Comfortable working in a fast-moving AI engineering environment.
- Ability to independently investigate new AI technologies and implement POCs/prototypes.
- Good understanding of software engineering principles, APIs, databases, security, and deployment.
- Strong communication and collaboration skills.
- Ability to work closely with an AI Architect / Technical Lead while taking ownership of implementation.
Skills Required
- Strong hands-on experience with Python
- Experience developing backend applications and REST APIs
- Experience with FastAPI
- Experience developing LLM and generative AI applications
- Experience designing and implementing RAG architectures and pipelines
- Experience with prompt engineering, embeddings, semantic search, and vector databases
- Experience integrating and orchestrating LLMs, including OpenAI, Anthropic, or Google Gemini
- Hands-on experience building AI agents and agent workflows
- Experience with LangGraph and/or LangChain
- Experience with tool or function calling, multi-agent systems, orchestration, state, and memory management
- Experience with PostgreSQL
- Experience with Docker and Azure and/or AWS
- Experience integrating APIs and microservices
- Experience with LLM and RAG evaluation, hallucination mitigation, AI guardrails, structured outputs, and schema validation
- Understanding of AI security and responsible AI practices
- Neo4j or GraphRAG experience
- Model Context Protocol experience
- Experience with vLLM, Ollama, or open-source LLMs such as Llama, Qwen, or Mistral
- Experience with LoRA, parameter-efficient fine-tuning, or model fine-tuning
- Kubernetes experience
- CI/CD experience with Azure DevOps or GitHub Actions
- React or Next.js experience
- Redis or other caching technology experience
- Experience with cloud-based AI/ML infrastructure and inference or LLM cost optimization
- Strong problem-solving, analytical, communication, and collaboration skills
- Ability to translate business requirements into practical AI solutions
- Ability to write clean, maintainable, production-quality code
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The Company
What We Do
Dash Technologies Inc. is a software development and IT services company that helps businesses modernize, scale, and grow. It builds enterprise-grade mobile, web, and custom software, while specializing in device engineering and AI/ML analytics. The company also develops healthcare products and describes its work as transforming healthcare delivery globally for organizations across multiple industries, combining technology expertise with customer-focused support.
