AI Engineer

Contract duration : 6 months

Salary :

3 years : 50-60K/month
4+ years : 75K/month
5-6years+ : 90K/month

Role Overview :

We are seeking an AI Engineer (5+ years experience) with expertise in LLMs, contextual autocomplete, and AI agent frameworks. You will design and implement AI-powered features such as code autocomplete, intelligent assistants for Kafka and Kubernetes, observability integrations, and extensible agent frameworks that interface with external systems and Condense backend APIs.

Key Responsibilities :

· Design and implement AI-powered features within Condense, including developer assistance, intelligent automation, and contextual data exploration.

· Integrate large language models (LLMs) and related technologies into Condense's platform in a scalable, secure, and cost-efficient way.

· Build and extend intelligent assistants/agents that interact with both Condense services (Kafka, pipelines, observability, cloud infrastructure) and external systems.

· Develop frameworks and APIs that enable extensibility, so customers and partners can plug in custom AI-powered capabilities.

· Collaborate with backend, frontend, and product teams to create intuitive AI-driven user experiences inside Condense's developer and operator workflows.

· Evaluate, fine-tune, and optimize LLMs, embeddings, and retrieval systems to provide context-aware completions and insights.

· Research emerging trends in applied AI for cloud, data streaming, and DevOps, and bring best practices into the product.

· Ensure reliability, performance, and cost-efficiency of AI workloads in multi-tenant, cloud-native deployments (BYOC model).

Required Qualifications :

· 4+ years of experience in AI/ML engineering, with focus on LLMs and intelligent assistants.

· Hands-on experience with LLM APIs (OpenAI, Anthropic, etc.) or open-source models (LLaMA, Mistral, Falcon, etc.).

· Strong programming skills in Python (primary) and familiarity with backend stacks (Java, Go, Node.js).

· Experience with retrieval-augmented generation (RAG), embeddings, and vector stores (Pinecone, Weaviate, Milvus, FAISS).

· Understanding of Kafka, and other real-time streaming systems.

· Knowledge of Kubernetes and cloud-native deployment patterns.

· Exposure to observability stacks (Prometheus, Grafana) and DevOps workflows.

· Strong problem-solving aptitude and ability to work across product, engineering, and UX teams.

Nice-to-Have Skills :

· Experience building AI-powered developer tools (autocomplete, code assistants, linters).

· Familiarity with LangChain, Semantic Kernel, or similar agent frameworks.

· Experience with multi-agent orchestration for complex workflows.

· Understanding of cost optimization for AI workloads in SaaS or BYOC deployments.

· Contributions to open-source AI, streaming, or DevOps ecosystems.