Deep Learning Senior Engineer

At Virtusa (Former ITMAGINATION), every innovator has the potential to transform and lead in a digital world. The company combines engineering excellence, creativity, and an AI-first mindset to co-create solutions that help businesses grow faster, operate smarter, and improve experiences with technology. The role is part of the AI delivery hub in Poland, focusing on advanced Generative AI solutions for global enterprise clients.

• 6–8 years of experience in Software Engineering or Machine Learning, with a minimum of 3 years focused on Deep Learning and NLP.
• Expert-level Python skills, including deep proficiency with scientific and AI libraries (NumPy, Pandas, PyTorch, or TensorFlow).
• Strong theoretical and practical understanding of Transformers, attention mechanisms, and semantic embeddings.
• Proven track record of building production-ready applications with LangChain, LlamaIndex, and LLM APIs (OpenAI, Anthropic, or Vertex AI).
• Hands-on experience with FAISS, Pinecone, or Weaviate, including indexing strategies, metadata filtering, and hybrid search optimization.
• Advanced experience with GCP, specifically Vertex AI (Model Garden, Pipelines, and Notebooks) and Cloud Storage.
• Deep understanding of NLP concepts such as tokenization, named entity recognition (NER), and semantic search logic.
• Experience using RAGAS or similar tools to quantify model performance (precision, recall, faithfulness).
• Practical knowledge of Docker and Kubernetes; familiarity with CI/CD for ML models and automated deployment workflows.
• Experience building scalable REST/GraphQL APIs and microservices for AI-driven applications.
• Understanding of Responsible AI practices, including data privacy (GDPR), PII masking, and bias detection in LLM outputs.
• Strong analytical problem-solving skills and the ability to work in an Agile (Jira) environment.
• Professional English (C1) is mandatory for collaboration with international technical leadership.
• Bachelor’s or Master’s degree in Computer Science, AI, Mathematics, or a related quantitative field.

Model Architecture & Design:

• Design and implement high-performance Generative AI applications utilizing Transformers, Diffusion models, and advanced NLP techniques.

LLM Orchestration:

• Build and manage complex, agent-based workflows using frameworks like LangChain and LlamaIndex to automate multi-step reasoning tasks.

Advanced RAG Systems:

• Architect end-to-end Retrieval-Augmented Generation (RAG) pipelines, integrating enterprise data with Vector Databases (Pinecone, FAISS, Weaviate) while ensuring high semantic relevance.

Google GenAI Mastery:

• Lead the implementation of Google Gemini models within the Vertex AI platform, optimizing for latency, throughput, and cost.

Fine-tuning & Optimization:

• Perform model fine-tuning, quantization, and embedding optimization to tailor LLMs to specific domain requirements and enterprise datasets.

Prompt Engineering & Evaluation:

• Design sophisticated prompt strategies and implement rigorous evaluation frameworks (e.g., RAGAS) to track model accuracy, hallucination rates, and drift.

Deployment & Scaling:

• Collaborate with MLOps teams to deploy models into production environments using Docker and Kubernetes, ensuring scalability and fault tolerance.