AI Engineer

Purpose

Responsible for designing, developing, and deploying production-grade AI solutions including autonomous agents, generative AI applications, and RAG-based systems. You will leverage large language models (LLMs), agentic frameworks, and advanced machine learning techniques to automate workflows, improve customer experiences, and drive business innovation.

Essential Duties & Responsibilities

• Design and deploy autonomous AI agents using frameworks like LangGraph, AutoGen, CrewAI, or OpenAI Assistants API for multi-step reasoning and task execution.
• Implement function calling, tool use, and API integrations enabling LLMs to interact with enterprise systems, databases, and external applications.
• Build advanced RAG systems with vector databases, hybrid search (dense + sparse retrieval), and reranking for domain-specific chatbots and knowledge retrieval.
• Develop generative AI solutions for text generation, summarization, audio-to-text transcription, and call center conversation insights using LLMs.
• Develop advanced prompting strategies including chain-of-thought reasoning, few-shot learning, and structured output generation.
• Integrate with AI platforms including Snowflake Cortex, OpenAI, Azure AI Studio, AWS Bedrock, and Anthropic Claude.
• Implement AI observability, guardrails, and evaluation frameworks (RAGAs, TruLens, DeepEval) to ensure quality, safety, and reliability.
• Conduct experiments and fine-tune models using techniques like LoRA and QLoRA to optimize performance for domain-specific use cases.
• Deploy production solutions using containerization (Docker, Kubernetes), CI/CD pipelines, and cloud-native architectures.
• Continuously monitor the performance of AI solutions and implement improvements.
• Create high-level and detailed design documentation for AI solutions, including architecture diagrams and technology selection rationale.
• Collaborate with cross-functional teams to identify and prioritize high-impact AI opportunities that drive significant business value.
• Mentor and provide guidance to junior team members; participate in code reviews and maintain high-quality engineering standards.
• Keep updated with advances in AI technology and find opportunities to upgrade existing solutions.
• Adhere to best practices in data privacy and security when working with sensitive data.

Qualifications and Education Requirements

• Minimum of 3 years of professional experience in AI engineering or related roles.
• 3+ years experience developing AI/ML solutions on platforms such as Snowflake, Azure, AWS , OpenAI, Databricks, or similar.
• 2+ years hands-on experience with Generative AI including LLM application development, RAG systems, and production deployments.
• Experience with agentic AI frameworks (LangGraph, AutoGen, CrewAI, OpenAI Assistants API) and multi-agent orchestration.
• Proficiency in Python, LangChain/LlamaIndex, and vector databases (Pinecone, Weaviate, Chroma, pgvector, Snowflake).
• Expertise in prompt engineering including chain-of-thought, few-shot learning, and structured outputs (JSON mode, function calling).
• Experience with evaluation frameworks for Generative AI (RAGAs, TruLens, DeepEval) in the context of text generation.
• Understanding of AI safety concepts including guardrails, content filtering, hallucination mitigation, and red-teaming.
• Experience with data preprocessing, feature engineering, and model evaluation techniques.
• Solid understanding of software engineering principles and best practices.
• Experience bringing GenAI projects through production and implementation with measurable business impact.

Soft Skills

• Strong analytical and problem-solving skills.
• Excellent communication skills with ability to articulate complex technical concepts to both technical and non-technical stakeholders.
• Highly motivated and self-driven with ability to work independently and in collaborative team environments.
• Able to think creatively about applying AI to solve business problems.
• Effective time management and organizational skills to manage multiple projects simultaneously.
• Continuous learning mindset and ability to adapt to new technologies in the fast-evolving AI landscape.