Senior AI Engineer

About the Role

Key Responsibilities

• Develop and implement AI features, applications, backend services, and integration components.
• Build and maintain LLM-based solutions, including chatbots, assistants, summarization, classification, extraction, and automation use cases.
• Implement RAG pipelines using documents, databases, APIs, vector stores, and search indexes.
• Integrate AI models with internal systems, applications, portals, workflows, and external platforms.
• Develop prompts, test model outputs, compare results, and improve AI response quality.
• Support the development of AI agents, workflow automation, tool-calling capabilities, and task-based AI services.
• Prepare, clean, transform, and validate datasets for AI processing, testing, and evaluation.
• Conduct model testing, benchmarking, output validation, and regression checks.
• Build APIs and services to expose AI functionality to applications and internal users.
• Work with infrastructure and DevOps teams to deploy AI solutions across development, testing, staging, and production environments.
• Document technical designs, APIs, prompts, workflows, models, assumptions, and implementation details.
• Troubleshoot AI performance, accuracy, integration, data quality, and deployment issues.
• Collaborate with product, business, security, infrastructure, and engineering teams.

Required skills & Qualifications

• 5+ years of experience in software engineering, AI engineering, machine learning, data engineering, or related technical roles.
• Hands-on experience with LLMs, prompt engineering, RAG, embeddings, AI APIs, and AI powered applications.
• Experience building AI automation solutions, AI assistants, or backend services that use AI models.
• Strong backend development experience, preferably using Python.
• Experience working with REST APIs, databases, integrations, queues, files, and enterprise systems.
• Good understanding of the software development lifecycle, Git, CI/CD, testing, and deployment practices.
• Experience with model evaluation, output testing, prompt optimization, and performance improvement is preferred.

Technical Skills

• Python and AI development libraries.
• LLM APIs and model ecosystems such as OpenAI, Azure OpenAI, Qwen, Llama, Mistral, Claude, or similar.
• RAG frameworks such as LangChain, LlamaIndex, Semantic Kernel, Haystack, or equivalent.
• Vector databases and search technologies such as Elasticsearch, OpenSearch, Qdrant, Milvus, Pinecone, Weaviate, or equivalent.
• Databases such as SQL Server, PostgreSQL, MongoDB, Elasticsearch, or similar.
• Docker and basic deployment knowledge across dev, test, staging, and production environments.
• Experience with data processing, document parsing, OCR, NLP, speech, or multimodal AI is a plus.
• Familiarity with cloud, private cloud, or on-prem AI environments is preferred.

Soft Skills & Competencies

• Strong analytical and problem-solving skills.
• Ability to work independently and deliver assigned tasks with quality.
• Good communication and documentation skills.
• Ability to work with cross-functional teams and follow technical guidance.
• Willingness to learn new AI tools, frameworks, models, and engineering practices.
• Strong attention to quality, security, accuracy, and maintainability.