AI Engineer

This is a fully remote position, open to applicants in India.

📋 Description

• Create, develop, and implement applications utilizing LLMs for functions such as text generation, summarization, classification, semantic search, and conversational AI.

• Fine-tune, prompt-engineer, and enhance LLMs to align with specific business needs while boosting accuracy, reducing latency, and increasing cost-efficiency.

• Construct and oversee comprehensive ML pipelines, encompassing data preprocessing, model training, evaluation, and deployment.

• Integrate LLM-driven solutions into production systems through APIs and microservices.

• Develop retrieval-augmented generation (RAG) systems employing vector databases and embeddings.

• Work collaboratively with stakeholders to transform business challenges into AI-powered solutions.

• Monitor model performance in live environments and adopt strategies for continuous improvement.

• Keep abreast of developments in LLMs, generative AI, and NLP research while applying industry best practices.

• Uphold responsible AI practices, including bias mitigation, explainability, and compliance with data privacy regulations.

⛳️ Requirements

• 3–8 years of experience in AI/ML engineering, specifically hands-on expertise in NLP and LLMs.

• Proficient programming skills in Python, with experience in frameworks like PyTorch, TensorFlow, or JAX.

• Practical experience dealing with LLMs (e.g., GPT-style models, open-source LLMs), including fine-tuning and prompt engineering.

• Familiarity with libraries such as Hugging Face Transformers, LangChain, or similar frameworks.

• Experience with vector databases (e.g., FAISS, Pinecone, Weaviate) and embedding methodologies.

• Strong grasp of machine learning principles, deep learning architectures, and NLP concepts.

• Experience utilizing cloud platforms (AWS, GCP, or Azure) and deploying scalable ML solutions.

• Knowledge of REST APIs, microservices architecture, and containerization tools like Docker/Kubernetes.

• Excellent problem-solving abilities and capability to thrive in a fast-paced, collaborative setting.

• Experience with retrieval-augmented generation (RAG) and knowledge-grounded AI systems.

• Exposure to reinforcement learning from human feedback (RLHF) or model alignment strategies.

• Familiarity with MLOps tools and practices (CI/CD for ML, monitoring, versioning).

🏝️ Benefits

• Contributions to open-source AI/ML projects or research publications in NLP/AI.