AI / ML Engineer
As an AI/ML Engineer at Nablon AI, you will be a hands-on technical contributor implementing agentic AI systems designed by our AI Architects. This role focuses on building production-grade ML models, implementing agent workflows, optimizing inference pipelines, and deploying scalable AI systems that run reliably in enterprise environments.
You'll work within a domain-specialized pod (FMCG/BFSI/Retail/Manufacturing/Automotive), translating architectural designs into working code, fine-tuning LLMs for specific use cases, building RAG pipelines, implementing agent orchestration logic, and ensuring systems meet performance, latency, and cost requirements. This is a deeply technical role where you'll write production Python code daily, debug complex distributed systems, and continuously optimize AI workloads.
Requirements:
- 2-5 years of experience in machine learning engineering, data science, or software engineering with significant exposure to AI/ML systems.
- Hands-on experience building and deploying machine learning models in production environments, not just notebooks or prototypes.
- Proven track record of writing production-quality Python code: clean architecture, proper testing, documentation, and version control practices.
- Strong understanding of machine learning fundamentals: supervised/unsupervised learning, model evaluation, overfitting, regularization, and cross-validation.
- Experience with deep learning frameworks (PyTorch or TensorFlow) and neural network architectures: transformers, CNNs, RNNs, or attention mechanisms.
- Hands-on experience with NLP tasks: text classification, named entity recognition, sentiment analysis, or language modeling.
- Proficiency with ML libraries and tools: Scikit-learn, Pandas, NumPy, Hugging Face Transformers.
- Experience working with LLM APIs (OpenAI, Anthropic, Google, or open-source models like LLaMA or Mistral) and an understanding of prompt engineering techniques.
- Familiarity with agentic AI frameworks (LangChain, LangGraph, LlamaIndex) or willingness to learn them quickly. We value learning ability over current expertise
- Understanding of RAG systems: vector embeddings, similarity search, retrieval strategies, and how to build context-aware LLM applications.
- Experience with at least one generative AI use case: chatbots, document Q& A, content generation, code generation, or similar applications.