What you'll do
- Design, build, and productionize AI and GenAI solutions for enterprise use cases
- Develop and optimize LLM-based applications, including RAG pipelines and agentic or workflow-based systems
- Integrate LLM capabilities into existing applications, APIs, and enterprise platforms
- Design and implement evaluation strategies to measure the quality, reliability, and performance of LLM applications
- Build production-ready solutions with appropriate testing, monitoring, observability, and CI/CD practices
- Collaborate with technical teams, business stakeholders, and international clients to translate ambiguous requirements into robust technical solutions
- Contribute to technical design decisions, architecture discussions, and engineering best practices
- Share knowledge, mentor colleagues, and contribute to the continuous evolution of GenAI engineering practices
Must Have
- 5+ years of professional software engineering experience, with significant hands-on experience in AI/ML/GenAI
- Advanced proficiency in Python and commonly used libraries for data processing, machine learning, and AI development
- Hands-on experience implementing and deploying LLM-based solutions in production
- Experience designing and building RAG architectures with vector databases
- Experience building LLM workflows or agentic systems using an orchestration framework
- Strong software engineering fundamentals, including API development, testing, system design, CI/CD, and production observability
- Hands-on experience designing or implementing evaluation strategies for LLM applications (retrieval quality, groundedness, hallucination detection, regression testing)
- Fluent English, required for working with international clients
Nice to have
- Experience with fine-tuning and model evaluation on cloud AI platforms
- Experience with AI quality, governance, and responsible AI practices
- Experience with model deployment in containers and MLOps practices
- Familiarity with more than one cloud ecosystem
Nice to have | Tech Skills
- Python & ML: Pandas, NumPy, Scikit-learn, PyTorch, TensorFlow
- LLMs: GPT-4, Claude, Llama 3, or equivalent models
- Vector Databases: ChromaDB, Pinecone, Weaviate, Milvus
- Orchestration Frameworks: LangChain, LlamaIndex, Haystack
- Cloud AI Platforms: IBM watsonx.ai, AWS Bedrock, Azure AI
- Evaluation Frameworks: Ragas, Giskard
- Containers & MLOps: Docker, Kubernetes
- Cloud Ecosystems: IBM Cloud, AWS, Azure
Soft Skills
- Enjoys technical knowledge sharing, mentoring, and continuous learning
- Strong analytical and problem-solving skills, with the ability to work effectively in ambiguous and evolving GenAI environments
- Comfortable communicating technical concepts to both technical and non-technical stakeholders
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