ETIC, AI Engineer - Senior Associate

Key Responsibilities 

• Design, develop, and optimize machine learning pipelines, including classical ML and deep learning models, using frameworks like scikit-learn, PyTorch, TensorFlow, and Hugging Face Transformers. 

• Lead development of retrieval-augmented generation (RAG) systems, integrating vector databases (e.g., FAISS, Weaviate, Qdrant) and frameworks like LangChain, LangGraph or LlamaIndex. 

• Fine-tune and adapt foundation models using techniques such as LoRA, PEFT, or instruction tuning for client-specific use cases. 

• Generate and evaluate synthetic data to support model robustness, augmentation, and privacy-aware training. 

• Build and expose AI models through RESTful APIs or microservices using FastAPI, Flask, or similar frameworks. 

• Ensure end-to-end model lifecycle tracking using tools like MLflow, Weights & Biases, or SageMaker Experiments. 

• Deploy models to cloud infrastructure (e.g., AWS, Azure, GCP) with attention to scalability, cost, and performance. 

• Collaborate with front-end developers and data teams to integrate AI features into products and platforms. 

• Review code, guide best practices in Git workflows, and contribute to team-level documentation and standards. 

• Communicate effectively with business stakeholders, translating complex AI concepts into actionable insights. 

Required Skills & Qualifications 

• Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics or related technical field. 

• 3+ years of experience developing AI/ML solutions in production environments. 

• Strong programming skills in Python, with a proven track record using libraries like NumPy, Pandas, scikit-learn, PyTorch, and Transformers. 

• Hands-on experience with LLMs (e.g., GPT, LLaMA, Mistral), prompt engineering, and GenAI application development. 

• Experience building RAG pipelines, integrating with vector databases, and working with LangChain, LlamaIndex, or similar frameworks. 

• Experience with utilizing multi-modal data for training ML models and building AI applications. 

• Familiarity with fine-tuning models and optimizing performance for NLP or computer vision tasks. 

• Understanding of MLOps practices, including containerization (Docker), CI/CD workflows, and model monitoring. 

• Experience deploying models on cloud platforms like AWS SageMaker, Bedrock, GCP Vertex AI, or Azure AI Services. 

• Strong communication skills and experience working in Agile teams or consulting environments.