AI/ML Engineer
Responsibilities:
Design, develop, and deploy end-to-end AI and machine learning solutions including LLM-powered applications, intelligent agents, and predictive systems.
Build and integrate AI APIs, RAG (Retrieval-Augmented Generation) pipelines, and agentic workflows using frameworks like LangChain, LlamaIndex, or custom implementations.
Fine-tune, prompt-engineer, and evaluate large language models (GPT-4, Claude, Gemini, Mistral, LLaMA) for domain-specific use cases.
Develop and maintain ML pipelines for data ingestion, preprocessing, model training, evaluation, and deployment.
Integrate AI capabilities into existing applications via RESTful APIs and microservices architecture.
Work with vector databases (Pinecone, Weaviate, ChromaDB, pgvector) for semantic search and knowledge retrieval systems.
Monitor, evaluate, and continuously improve model performance, accuracy, and cost-efficiency in production environments.
Collaborate with product, engineering, and data teams to translate business problems into AI-driven solutions.
Implement responsible AI practices including bias detection, output validation, and safety guardrails.
Manage model versioning, experiment tracking (MLflow, Weights & Biases), and CI/CD pipelines for ML systems.
Required Qualifications:
Bachelor's degree (or equivalent) in Computer Science, Artificial Intelligence, Machine Learning, or related field.
2+ years of hands-on experience building and deploying AI/ML systems in production environments.
Strong proficiency in Python with experience in AI/ML libraries (PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn).
Demonstrated experience working with large language models — including prompt engineering, fine-tuning, and evaluation.
Familiarity with LLM orchestration frameworks such as LangChain, LlamaIndex, or CrewAI.
Experience with vector databases and embedding models for semantic search or RAG pipelines.
Solid understanding of ML fundamentals: supervised/unsupervised learning, neural networks, NLP, and model evaluation.
Experience with cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML) and containerization (Docker, Kubernetes).
Proficiency with Git, collaborative development workflows, and experiment tracking tools.
Strong communication skills — able to explain complex AI concepts to non-technical stakeholders.
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Our email: hr@puretreesolutions.com