Responsibilities
- Design, build, and deploy LLM-based applications and agentic systems to production, from prototype to live system;
- Implement RAG pipelines, tool-using agents, and multi-step workflows over proprietary and third-party data sources;
- Evaluate foundation models (OpenAI, Anthropic, Gemini, Llama, and others) against client use cases, constraints, and budget;
- Build and maintain LLM evaluation pipelines, including offline benchmarks, online monitoring, and human-in-the-loop review processes;
- Measure and improve model performance iteratively across quality, relevance, faithfulness, latency, and cost;
- Deploy and serve AI systems on cloud platforms with attention to scalability, reliability, and cost efficiency;
- Work with Solution Architects and delivery teammates to translate ambiguous business problems into concrete technical tasks and solution components;
- Communicate technical decisions, limitations, and trade-offs clearly to teammates, product stakeholders, and clients when needed;
- Contribute reusable internal tools, skills, integrations, and MCP-style patterns that improve future client delivery;
- Stay current with the fast-moving AI landscape and evaluate emerging frameworks, tools, and approaches with a practical mindset;
- Making customization for the client.
Basic qualifications
- 5+ years of primary software engineering experience building and shipping production systems;
- 2+ years of hands-on experience building and deploying LLM-based applications or AI agents in production environments;
- Strong software engineering fundamentals and the ability to write clean, maintainable, production-ready code;
- Experience building APIs, services, integrations, and data flows for production systems
- Experience designing and implementing agentic systems using major model providers and open-weight models (e.g. OpenAI, Anthropic, Gemini, Llama, or comparable);
- Hands-on experience with at least one orchestration framework (LangChain, LangGraph, AutoGen, CrewAI, etc.);
- Experience with LLM evaluation and benchmarking: designing evaluation pipelines, measuring model performance, and iterating systematically;
- Experience architecting RAG systems, including chunking strategies, embedding models, vector databases, and reranking (e.g. Pinecone, Weaviate, Qdrant, Chroma, Milvus);
- Strong prompt design and iteration skills;
- Experience building custom tools, skills, integrations, or protocol-based extensions for AI systems, including MCP-style server/client patterns;
- Cloud platform experience with AWS and/or GCP (Vertex AI) and/or Azure AI Foundry (formerly Azure AI Studio);
- Familiarity with AI safety, responsible AI, and output guardrails for production systems;
- Experience with testing, observability, monitoring, and optimization for latency, throughput, reliability, and cost in production AI systems;
- Experience working with data platforms relevant to AI workloads, such as Redis, Cassandra, BigQuery, Postgres, or similar systems;
- Upper- intermediate level of English.
- Experience building or fine-tuning models for domain-specific use cases (e.g. financial data, ticketing);
- Experience building custom agent frameworks from scratch;
- Experience with agent evaluation frameworks (LangSmith, AgentEvals, etc.);
- Portfolio of delivered AI projects: production applications, demos, experiments, blog posts, or open-source contributions;
- Machine learning and deep learning fundamentals: model training, evaluation, regularization, and optimization (e.g. PyTorch, TensorFlow, JAX);
- Hands-on experience with AI domains beyond LLMs, such as computer vision, recommendation and personalization systems, forecasting and time-series modeling, or generative AI;
- Experience applying AI safety and guardrails in regulated or high-sensitivity environments.
About the company
We are building a bench of Applied AI Engineers to support client engagements whenever expert AI engineering capability is needed. This is not a role tied to a single product, stack, or domain. We are looking for engineers who can work confidently across modern applied AI systems, especially LLM-based applications, agentic workflows, and adjacent AI capabilities where relevant to the project.
The ideal candidate combines strong software engineering fundamentals with hands-on applied AI delivery experience. They know when to use cutting-edge techniques and, just as importantly, when to choose simpler approaches. They can take AI features from prototype to production and work effectively across different client contexts, tech stacks, and delivery constraints.
This role is not isolated. You can work alongside our Solution Architect and broader delivery team. We are looking for someone who can independently build and ship, while collaborating on architecture, trade-offs, and client delivery as part of a larger team.
This role offers exposure to varied client problems, real production delivery, and the opportunity to work across model providers, tooling ecosystems, and industries. It is a strong fit for engineers who enjoy practical problem-solving, ownership, and continuous learning in a fast-moving field.