Original listing text, shown exactly as published by the company.
Responsibilities
AI Strategy & Solution Design
- Identify opportunities to apply AI across software development, business operations, and enterprise workflows.
- Evaluate business problems and determine when AI is (or is not) the appropriate solution.
- Design scalable AI architectures aligned with business goals, security requirements, and operational constraints.
- Guide clients through AI adoption and implementation strategies.
AI Engineering & Architecture
- Design and implement solutions using Generative AI, LLMs, RAG, agentic workflows, and AI orchestration frameworks.
- Define architecture patterns for AI-enabled applications and enterprise platforms.
- Establish best practices for prompt engineering, retrieval strategies, evaluation, and AI governance.
- Design solutions that balance quality, latency, reliability, and operational cost.
SDLC Transformation
- Drive adoption of AI capabilities across the software development lifecycle.
- Identify opportunities for AI-assisted development, automated documentation, testing, code review, deployment validation, and engineering productivity improvements.
- Collaborate with engineering teams to integrate AI into development processes and delivery pipelines.
Technical Leadership
- Lead technical discussions with clients and internal stakeholders.
- Mentor engineering teams on AI implementation patterns and best practices.
- Support architecture reviews, solution estimates, and technical decision-making.
- Contribute to the development of reusable AI accelerators and frameworks.
Required Qualifications
- Strong software engineering background with hands-on experience in Python and modern backend development.
- Experience designing and deploying AI solutions in production environments.
- Practical experience with Generative AI, LLMs, RAG architectures, AI agents, and orchestration frameworks.
- Experience integrating AI capabilities into enterprise applications and workflows.
- Understanding of AI evaluation, observability, monitoring, and operational best practices.
- Experience balancing AI quality, latency, scalability, reliability, and cost considerations.
- Strong architecture and system design skills.
- Excellent communication skills and ability to engage with technical and non-technical stakeholders.
- Advanced English proficiency.
Preferred Qualifications
- Experience with cloud platforms such as AWS, Azure, or GCP.
- Experience with MLOps, CI/CD, and AI deployment pipelines.
- Experience implementing AI capabilities across the SDLC.
- Knowledge of vector databases, knowledge retrieval systems, and GraphRAG architectures.
- Experience leading technical teams or acting as a technical lead or architect.
- Experience supporting pre-sales activities, solution discovery, or client workshops.
What Success Looks Like
- Identify high-value AI opportunities and translate them into production-ready solutions.
- Drive measurable improvements in engineering productivity and business outcomes.
- Establish scalable AI implementation patterns and governance practices.
- Enable teams and clients to successfully adopt AI across their development and operational processes.
