AI - Specialist

1 week ago

Timișoara, Timiş, Romania Ness Digital Engineering Full time 120,000 - 180,000 per year
Why Join Ness? At Ness, we believe our people are our greatest asset. Our culture of innovation, technical excellence, and collaboration drives successful outcomes for some of the world's most advanced software and platform companies. You'll work on cutting-edge modernization initiatives leveraging generative AI, AI/ML, and cloud-native technologies alongside some of the most talented minds in the industry. You'll enjoy accelerated growth, continuous learning, access to certifications, and a dynamic environment where your work truly makes an impact.   About the Role We are looking for a highly skilled and hands-on Lead AI Engineer to join our Center of Practice (COP) and lead the modernization of legacy applications into moden architectures and tech stacks. The selected candidate will architect and implement GenAI-powered solutions using Amazon Bedrock and open-source LLMs, and work with our technical team to create an automated, scalable modernization framework. This is a critical, high-priority role with direct involvement in a strategic transformation program for one of our key clients.   Key Responsibilities AI-Powered Code Modernization
  • Lead the design and implementation of AI-driven workflows to transform legacy Azure Data Factory code into Python using fine-tuned LLMs.
  • Evaluate and integrate GenAI services from platforms like Amazon Bedrock, and/or open-source models (e.g., LLaMA, Mistral, GPT-J).
  • Fine-tune models for enhanced accuracy and contextual transformation quality specific to the client's codebase and architectural patterns.
Technical Architecture & Strategy
  • Define the AI architecture for the modernization platform including RAG, model orchestration, and deployment pipelines.
  • Build intelligent agent-based systems to analyze, segment, and refactor large codebases.
  • Collaborate with DevOps, Product, and Architecture teams to ensure security, scalability, and maintainability of the AI workflows.
AI Engineering and Model Deployment
  • Implement and manage LLMOps pipelines for continuous evaluation and tuning of models.
  • Create prompt engineering strategies and vectorized knowledge bases to support transformation decisioning.
  • Establish benchmarking and validation frameworks to ensure high fidelity and accuracy of the code translation process.
Collaboration & Leadership
  • Work closely with engineering, product, and customer teams to align on business objectives and technical execution.
  • Guide junior AI engineers and contribute to growing AI maturity across the practice.
  • Participate in customer workshops, POCs, and roadmap planning sessions.
  Qualifications Technical Skills
  • Strong hands-on experience with Generative AI models and LLM fine-tuning.
  • Proven expertise in code transformation projects or tooling (e.g., source-to-source translation, code embeddings, ASTs).
  • Proficiency in Python, AI/ML libraries, and frameworks like PyTorch, TensorFlow.
  • Experience with LangChain, LlamaIndex, LangGraph, or similar orchestration tools.
  • Familiarity with Amazon Bedrock, SageMaker, or open-source LLM stacks.
  • Solid understanding of software engineering principles in C++ and Java.
  • Experience with Docker, Kubernetes, and cloud-native deployment (preferably AWS).
Leadership & Strategy
  • Experience in leading AI/ML teams and owning end-to-end delivery of AI solutions.
  • Strong architectural and problem-solving skills, especially in enterprise transformation contexts.
  • Ability to articulate complex AI strategies to both technical and non-technical stakeholders.
  Preferred Experience
  • Previous experience in modernizing legacy systems or working in large-scale software refactoring initiatives.
  • Exposure to code comprehension, static analysis tools, or AI-powered development platforms.
  • Working knowledge of DevOps pipelines and CI/CD for ML models.
  What We Offer
  • Challenging and impactful work that pushes the boundaries of what AI can do in software engineering.
  • Competitive compensation and benefits.
  • Access to the latest technologies, certifications, and learning platforms.
  • Inclusive, diverse, and supportive work culture.
 

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