AI/ML Engineer

We are looking for a Senior AI/ML Engineer. You will serve as the core data and machine-learning authority during client engagements, proposing advanced AI architectures and data pipelines. In the delivery phase, you will be hands-on, writing the heavy Python code required to implement RAG systems, configure vector databases, and fine-tune machine learning models., * Architecture & Proposals: Consult with clients to assess their data readiness, recommend optimal ML approaches (e.g., prompt engineering vs. fine-tuning), and design scalable AI architectures.

• Hands-On AI Development: Build and optimize complex RAG architectures, multi-agent systems, and semantic search capabilities.
• Data Engineering: Design data ingestion, chunking, and embedding pipelines to feed intelligence into the applications.
• Model Management & Evaluation: Handle model selection, parameter-efficient fine-tuning (PEFT/LoRA), and deployment configuration. Continuously evaluate model performance and iterate based on metrics.
• Production Reliability & Monitoring: Ensure ongoing model scalability, reliability, and robust monitoring in production environments.
• Performance & Cost Optimization: Proactively identify and implement strategies to optimize both the computational performance and operational costs of AI systems.

• 5+ years of professional Machine Learning or Data Engineering experience, featuring deep expertise and strong coding skills in Python.
• Production experience with ML frameworks (PyTorch, TensorFlow) and comprehensive knowledge of LLM ecosystems (OpenAI, Hugging Face Transformers, LangChain).
• Hands-on experience building and deploying RAG pipelines and managing Vector Databases (Pinecone, Milvus, Weaviate, Qdrant, or pgvector).
• Experience with cloud platforms (AWS, GCP, Azure).
• Knowledge of data engineering tools (such as Apache Airflow and Spark).
• Understanding of MLOps and collaborative development, including model versioning, monitoring, GitHub, and CI/CD practices.
• Experience with system integrations and orchestration.
• Strong communication skills, with the ability to articulate complex data science concepts and architectural trade-offs to client-side technical leadership (CTOs, Lead Data Scientists)., * Experience with open-source model deployment (Llama 3, Mistral) and fine-tuning techniques.
• Familiarity with managed cloud AI services (AWS SageMaker, Vertex AI, or Azure AI).