Skills

Must have

4+ years of experience in AI/ML, including Deep Learning and Reinforcement Learning.
Strong Python engineering experience, preferably Python 3.12+.
Strong hands-on experience with agentic frameworks (LangChain, LangGraph, CrewAI).
Experience building LLM applications with tool/function calling and structured outputs.
Hands-on experience with OpenAI APIs or similar model providers such as Gemini, Anthropic, or local models.
Practical experience of agent frameworks or orchestration patterns, such as Agno, LangGraph, CrewAI, AutoGen, or similar.
Solid understanding of vector databases(Milvus, Pinecone, Qdrant), graph design, and RAG architectures.
Experience designing scalable, production-grade AI systems.
Ability to design robust prompts, agent instructions, guardrails, and evaluation datasets.
Experience with A2A protocol, AG-UI protocol, MCP, or multi-agent communication systems.
Experience integrating AI systems with business APIs and databases.
Strong testing mindset, including unit tests, integration tests, and LLM/agent behaviour evaluations.
Familiarity with Redis, Postgres, object storage, Docker, and cloud-native deployment basics.
Good judgment around security, guardrails, privacy, observability, and production reliability.
Ability to work in ambiguous, fast-changing environments with minimal supervision.
Experience with Kubernetes, Helm, CI/CD, and production observability.
Experience with Temporal workflows.
Strong problem-solving mindset with a bias for action.

4+ years of experience in AI/ML, including Deep Learning and Reinforcement Learning.
Strong Python engineering experience, preferably Python 3.12+.
Strong hands-on experience with agentic frameworks (LangChain, LangGraph, CrewAI).
Experience building LLM applications with tool/function calling and structured outputs.
Hands-on experience with OpenAI APIs or similar model providers such as Gemini, Anthropic, or local models.
Practical experience of agent frameworks or orchestration patterns, such as Agno, LangGraph, CrewAI, AutoGen, or similar.
Solid understanding of vector databases(Milvus, Pinecone, Qdrant), graph design, and RAG architectures.
Experience designing scalable, production-grade AI systems.
Ability to design robust prompts, agent instructions, guardrails, and evaluation datasets.
Experience with A2A protocol, AG-UI protocol, MCP, or multi-agent communication systems.
Experience integrating AI systems with business APIs and databases.
Strong testing mindset, including unit tests, integration tests, and LLM/agent behaviour evaluations.
Familiarity with Redis, Postgres, object storage, Docker, and cloud-native deployment basics.
Good judgment around security, guardrails, privacy, observability, and production reliability.
Ability to work in ambiguous, fast-changing environments with minimal supervision.
Experience with Kubernetes, Helm, CI/CD, and production observability.
Experience with Temporal workflows.
Strong problem-solving mindset with a bias for action.

AI Engineer

Compensation

Not disclosed

Location

Remote

Work setup

Remote

Seniority

Not specified

Employment

Not specified

Experience

4+ years

4+ years of experience in AI/ML, including Deep Learning and Reinforcement Learning.
Strong Python engineering experience, preferably Python 3.12+.
Strong hands-on experience with agentic frameworks (LangChain, LangGraph, CrewAI).
Experience building LLM applications with tool/function calling and structured outputs.
Hands-on experience with OpenAI APIs or similar model providers such as Gemini, Anthropic, or local models.
Practical experience of agent frameworks or orchestration patterns, such as Agno, LangGraph, CrewAI, AutoGen, or similar.
Solid understanding of vector databases(Milvus, Pinecone, Qdrant), graph design, and RAG architectures.
Experience designing scalable, production-grade AI systems.
Ability to design robust prompts, agent instructions, guardrails, and evaluation datasets.
Experience with A2A protocol, AG-UI protocol, MCP, or multi-agent communication systems.
Experience integrating AI systems with business APIs and databases.
Strong testing mindset, including unit tests, integration tests, and LLM/agent behaviour evaluations.
Familiarity with Redis, Postgres, object storage, Docker, and cloud-native deployment basics.
Good judgment around security, guardrails, privacy, observability, and production reliability.
Ability to work in ambiguous, fast-changing environments with minimal supervision.
Experience with Kubernetes, Helm, CI/CD, and production observability.
Experience with Temporal workflows.
Strong problem-solving mindset with a bias for action.

4+ years of experience in AI/ML, including Deep Learning and Reinforcement Learning.
Strong Python engineering experience, preferably Python 3.12+.
Strong hands-on experience with agentic frameworks (LangChain, LangGraph, CrewAI).
Experience building LLM applications with tool/function calling and structured outputs.
Hands-on experience with OpenAI APIs or similar model providers such as Gemini, Anthropic, or local models.
Practical experience of agent frameworks or orchestration patterns, such as Agno, LangGraph, CrewAI, AutoGen, or similar.
Solid understanding of vector databases(Milvus, Pinecone, Qdrant), graph design, and RAG architectures.
Experience designing scalable, production-grade AI systems.
Ability to design robust prompts, agent instructions, guardrails, and evaluation datasets.
Experience with A2A protocol, AG-UI protocol, MCP, or multi-agent communication systems.
Experience integrating AI systems with business APIs and databases.
Strong testing mindset, including unit tests, integration tests, and LLM/agent behaviour evaluations.
Familiarity with Redis, Postgres, object storage, Docker, and cloud-native deployment basics.
Good judgment around security, guardrails, privacy, observability, and production reliability.
Ability to work in ambiguous, fast-changing environments with minimal supervision.
Experience with Kubernetes, Helm, CI/CD, and production observability.
Experience with Temporal workflows.
Strong problem-solving mindset with a bias for action.