Role overview
We are seeking a highly skilled
Generative AI / Machine Learning Engineer
with strong expertise in
Natural Language Processing (NLP)
to design, develop, and deploy AI-driven solutions. This role will focus on building scalable ML systems, fine-tuning large language models (LLMs), and implementing NLP pipelines that power enterprise applications.
The ideal candidate combines strong theoretical ML knowledge with hands-on engineering experience in modern AI frameworks and cloud-based ML infrastructure.
Responsibilities
- Design, develop, and deploy NLP and Generative AI solutions in production environments
- Fine-tune and optimize Large Language Models (LLMs) for domain-specific use cases
- Build and maintain ML pipelines for data ingestion, preprocessing, training, and inference
- Develop prompt engineering strategies and evaluate model performance
- Implement Retrieval-Augmented Generation (RAG) architectures
- Work with structured and unstructured text datasets
- Conduct model evaluation, error analysis, and performance tuning
- Collaborate with data engineers and software teams to integrate AI models into applications
- Ensure responsible AI practices including bias mitigation, explainability, and governance
- Maintain documentation and contribute to AI best practices and architecture standards
Basic qualifications
- Bachelorās or Masterās degree in Computer Science, Machine Learning, Data Science, or related field
- 5+ years of experience in Machine Learning or AI engineering
- 3+ years of hands-on experience with NLP
- Strong programming skills in Python
- Experience with ML frameworks such as:
- PyTorch
- TensorFlow
- Scikit-learn
- Experience working with:
- Hugging Face Transformers
- OpenAI / LLM APIs
- LangChain or similar orchestration frameworks
- Experience building and deploying models in cloud environments (AWS, Azure, or GCP)
- Knowledge of vector databases (e.g., Pinecone, FAISS, Weaviate)
- Strong understanding of:
- Embeddings
- Tokenization
- Text classification
- Named Entity Recognition (NER)
- Sentiment analysis
- Semantic search
- Experience with REST APIs and microservices architecture
- Familiarity with CI/CD pipelines for ML deployment
Preferred qualifications
- Experience with:
- RAG architectures
- LLM fine-tuning (LoRA, PEFT, etc.)
- Distributed training
- MLOps tools (MLflow, Kubeflow, SageMaker)
- Experience working in regulated or government environments
- Exposure to AI governance and compliance frameworks
- Experience handling sensitive or classified datasets
Tags & focus areas
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