AI Engineer

• Design, develop, and maintain generative AI services and reusable components using mostly Python and a little bit of Java.
• Define and promote best practices in engineering, including scalability, observability, testing, and CI/CD.
• Contribute to system designs spanning multiple services and modules, aligning with architectural best practices.
• Collaborate with product, platform, and research teams to translate AI prototypes into production-ready capabilities.
• Work within a Kubernetes (EKS) environment to deploy scalable, containerized applications.
• Contribute to the resolution of complex technical challenges across distributed systems.
• Mentor less-senior developers on engineering principles, GenAI patterns, and platform development.
• Participate in code reviews, architecture sessions, and cross-team initiatives to ensure quality and maintainability.
• Stay informed of the latest developments in generative AI and advocate for responsible integration into product ecosystems.

Senior Software Engineer – AI Engineering

• Take up a tech lead role on a per-project basis
• Design and build robust software systems for end-to-end (Gen)AI solutions
• Ensure the scalability of deployed systems
• Lead teams of engineers and translate business challenges into technical solutions

Senior GenAI Engineer – Emerging

• Lead technical discovery and experimentation, translating loosely defined opportunities into concrete solution approaches
• Design and execute rapid experiments and pilots to validate feasibility and value
• Work closely with Product Manager, Solution Architect and UX Designer to ensure solutions are technically feasible, usable, and aligned with real workflows
• Collaborate with the Product Manager to shape problem definition, prioritization, and solution direction
• Act as the technical AI lead within a small, cross-functional experimentation team
• Work directly with domain experts to understand workflows, constraints, and opportunities
• Take ownership of moving pilots into structured implementations and working solutions
• Help define solution architecture, data requirements, model approach, and integration patterns
• Build initial production-ready solutions where required to accelerate delivery
• Introduce structure across early-stage initiatives, including clear milestones and decision points
• Provide clear input on technical feasibility, trade-offs, and scalability
• Work with data engineering, platform, and product teams to scale and integrate solutions
• Ensure solutions are reliable, explainable, and aligned with enterprise and Responsible AI standards
• Support adoption by communicating clearly and bringing teams along

Staff AI Engineer – AI Studio, Scalable AI Solutions

• Act as a hands-on engineer embedded within client teams, designing and deploying end-to-end AI/ML systems from proof-of-concept to production.
• Own a feature or deployment pipeline on client projects, demonstrating technical mastery and end-to-end responsibility.
• Spearhead the adoption of MLOps/LLMOps best practices - feature stores, model monitoring, pipeline orchestration — to ensure client systems are scalable and reliable.
• Collaborate with Data Scientists and Software Engineers to design and implement robust data pipelines and model APIs.
• Bridge the gap between data science and business value by translating technical findings into clear, actionable insights for non-technical stakeholders.
• Identify new tools, methods, and processes that work best — and share them with the team.
• Help in the interview process for additional AI Engineers joining Enjins' growing team.
• Have meaningfully accelerated your client's velocity.

• Partner with C-level stakeholders to design AI roadmaps that aren't just 'projects,' but scalable technical assets that drive core business value.
• Ensure the long-term health of deployed solutions by implementing rigorous MLOps standards, keeping our 'engines' high-performing and maintainable.
• Lead the AI Engineering team alongside other Leads, fostering a culture of technical excellence and pragmatic innovation.
• Involve yourself in business-critical decisions on team growth, innovation roadmaps, and account management.
• Within 1 Month: Complete Enjins leadership onboarding and become a certified cloud practitioner.
• Within 3 Months: Own the technical roadmap for at least one client engagement, from architecture to production.
• Within 6 Months: Drive meaningful client velocity and build relationships with C-level stakeholders.

Lead AI Engineer

• Design, implement, and maintain scalable cloud-based data lake and pipelines, leveraging Databricks for robust, high-performance solutions.
• Develop efficient data pipelines for ingesting, processing, and storing large datasets, incorporating best practices for data quality, lineage tracking, and metadata management - specifically optimized for LLM consumption.
• Enforce data quality checks, monitor pipeline health, and continuously optimize reliability, scalability, and performance.
• RAG & Agentic Systems: Move beyond "naive RAG" to implement hybrid search, re-ranking, and contextual compression to handle dense financial documents and tables.
• Agentic Workflows: Design and deploy multi-agent systems using LangGraph, CrewAI, or Agent Bricks.
• Evaluation: Implement rigorous "LLM-as-a-Judge" frameworks to quantify accuracy, faithfulness, and latency.
• Deploy security layers to ensure AI outputs comply with financial regulations and privacy standards.
• Create reusable "Agent templates" that empower Finance business partners to build their own safe AI-driven workflows.
• Work closely with AI teams and key stakeholders across finance to understand data requirements for AI systems and ensure their smooth deployment.
• Provide support and guidance on data engineering best practices and tools to other team members.
• Guide business functions and personnel in developing do-it-yourself capabilities for data tasks.

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