AI Engineer - Data42

Key Responsibilities

• Design and deliver end-to-end AI-powered applications, from discovery and architecture through implementation, deployment, monitoring, and continuous improvement. 

• Build agentic AI solutions using large language models, tool use, structured workflows, retrieval-augmented generation, memory and context patterns, orchestration, and human-in-the-loop controls. 

• Engineer and productionize machine learning solutions, including data preparation, feature engineering, model training or adaptation, evaluation, deployment, inference, monitoring, and retraining workflows. 

• Develop reliable backend services and APIs using Python and modern service patterns; build intuitive web experiences using TypeScript, React, and contemporary UI frameworks where required. 

• Integrate enterprise data sources, APIs, vector stores, model endpoints, and business systems into secure AI applications and automated workflows. 

• Design robust system architectures spanning frontend applications, backend services, data pipelines, ML services, agent orchestration, observability, and deployment infrastructure. 

• Create evaluation frameworks for AI quality, grounding, task completion, safety, latency, cost, and business impact; use results to improve prompts, models, retrieval, tools, and workflows. 

• Implement responsible AI and security controls, including access control, data protection, auditability, guardrails, traceability, human oversight, and compliance-by-design. 

• Apply software engineering discipline through automated testing, version control, code review, CI/CD, infrastructure configuration, release management, and production support. 

• Operate production AI services with appropriate logging, tracing, alerting, incident response, performance optimization, reliability controls, and service documentation. 

• Partner with product managers, data scientists, domain experts, architects, UX designers, platform teams, and governance stakeholders to convert complex requirements into measurable product outcomes. 

• Provide technical guidance, reusable patterns, documentation, and engineering best practices that accelerate safe adoption of AI across teams. 

Minimum Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field, a Master's degree is preferred. 

• Typically 5+ years of professional software, data, or ML engineering experience, including meaningful hands-on delivery of AI/ML applications in production environments. 

• Strong proficiency in Python and practical experience with software architecture, APIs, data structures, testing, and maintainable application design. 

• Hands-on experience implementing machine learning lifecycle activities such as experimentation, feature/data pipelines, model evaluation, deployment, inference, and monitoring. 

• Hands-on experience building applications with LLMs or agentic AI patterns, including prompt and context design, tool integration, retrieval, workflow orchestration, evaluation, and guardrails. 

• Experience with cloud-native or enterprise platforms and production engineering practices, including containers, CI/CD, environment management, secrets, identity and access controls, and observability. 

• Working knowledge of modern data technologies such as SQL, Spark or distributed processing, data lakes or warehouses, and heterogeneous data integration. 

• Ability to design for non-functional requirements including security, privacy, scalability, reliability, maintainability, performance, and cost. 

• Proven ability to translate ambiguous business or scientific needs into technical designs, working increments, measurable acceptance criteria, and production outcomes. 

• Clear communication, collaboration, and documentation skills, with a product-minded and iterative approach to delivery.