AI/ML Engineer
First seen by Alion on Jul 22, 2026.
AI/ML Developer (Mid-Level) - NLP & Generative AI
Experience : 5+ Years
About the Role:
We are looking for an AI/ML Developer with a strong focus on Natural Language Processing (NLP) and Generative AI to join our team. You will design, build, and deploy ML/LLM-based solutions that power intelligent features across our products, including chatbots, semantic search, document understanding, and generative content applications.
Experience Required :
- Hands-on experience in Machine Learning / Deep Learning development
- At least 2 or more years of specific experience working with NLP and/or LLM-based systems
Key Responsibilities :
- Design, develop, and fine-tune NLP models for tasks such as text classification, NER, summarization, and question-answering
- Build and optimize applications using Large Language Models (LLMs), including prompt engineering, fine-tuning, and RAG (Retrieval-Augmented Generation) pipelines
- Develop and maintain vector search/embedding-based retrieval systems
- Collaborate with product and engineering teams to integrate ML/LLM capabilities into production applications
- Evaluate and benchmark model performance (accuracy, latency, cost, hallucination rate)
- Deploy and monitor models in production environments
- Stay current with the latest research and tools in NLP and Generative AI, and evaluate their applicability to business use cases
Required Skills :
Programming & Core Technical :
- Strong proficiency in Python
- Experience with SQL for data querying
- Solid understanding of data structures, algorithms, and object-oriented programming
NLP & LLM Specific :
- Hands-on experience with Hugging Face Transformers
- Experience with LLMs (OpenAI, Anthropic Claude, Llama, Mistral, etc.) via APIs or self-hosted deployment
- Prompt engineering and prompt optimization techniques
- Experience fine-tuning LLMs (LoRA, QLoRA, PEFT) or training custom NLP models
- RAG (Retrieval-Augmented Generation) architecture and implementation
- Vector databases (Pinecone, FAISS, or similar)
- Text embeddings and semantic search
- Tokenization, text preprocessing, and language model evaluation metrics
ML/DL Frameworks & Libraries :
- PyTorch and/or TensorFlow
- Scikit-learn
- Pandas, NumPy
- LangChain, LlamaIndex, or similar LLM orchestration frameworks
ML Fundamentals :
- Supervised/unsupervised learning, classification, clustering
- Model evaluation and validation techniques
- Feature engineering
- Understanding of overfitting, bias-variance tradeoff, and regularization
MLOps & Deployment :
- Experience deploying models via REST APIs (FastAPI/Flask)
- Containerization with Docker
- Cloud platforms - AWS, GCP, or Azure
- Model versioning and experiment tracking (MLflow, Weights & Biases, DVC)
Data Handling :
- Data cleaning, preprocessing, and pipeline development
- Experience working with large-scale unstructured text datasets
- Familiarity with distributed data processing (Spark or Dask) is a plus
Tools & Version Control :
- Git/GitHub or GitLab
- Jupyter Notebooks
- Familiarity with Agile/Scrum development practices
Skills
Artificial Intelligence, Machine Learning, NLP, Generative AI, LLM, Deep Learning, SQL, Python, LORA, PyTorch, Tensorflow