AI Engineer

Job Title: AI/LLM Engineer

Job Description:

We are seeking a skilled and enthusiastic AI/LLM Engineer to join our team and help design, build, and deploy advanced AI systems using cutting-edge large language models (LLMs). The role will involve working with state-of-the-art technologies such as Langchain, Vector Databases, and Generative AI to create scalable solutions that drive real-world impact. You will play a key role in fine-tuning, integrating, and deploying LLMs across diverse applications, ranging from chatbots to content generation, search, and beyond.

Key Responsibilities:

· Develop and Fine-tune LLMs: Work with language models such as GPT, BERT, T5, and others to fine-tune them for specific use cases like natural language understanding, text generation, and conversation systems.

· Langchain Integration: Utilize Langchain to create pipelines and frameworks that integrate LLMs with external data sources, APIs, and tools, enabling more dynamic and contextually aware AI applications.

· Vector Databases: Implement and manage vector databases (e.g., Pinecone, FAISS, Weaviate) to store and retrieve high-dimensional vector embeddings for tasks like semantic search, document retrieval, and knowledge-based query answering.

· Research & Experimentation: Stay at the forefront of AI and NLP research to experiment with new techniques and methodologies. Innovate on model fine-tuning, prompt engineering, and retrieval-augmented generation (RAG) techniques.

· Model Deployment & MLOps: Build and deploy LLM-powered solutions in production environments, ensuring performance optimization and scalability. Leverage MLOps practices to automate the end-to-end lifecycle of models, from training to deployment and monitoring.

· Data Management & Preprocessing: Handle large datasets for training and inference, including data collection, preprocessing, and augmentation. Develop and apply tokenization techniques and manage large-scale text data.

· Collaborate on AI Solutions: Work closely with cross-functional teams, including data scientists, machine learning engineers, and product developers, to translate business problems into technical solutions using LLMs and AI.

· Optimize Vector Search: Develop efficient vector-based search solutions that allow for semantic retrieval of information using embeddings generated from language models.

· Prompt Engineering: Experiment with prompt engineering and retrieval-based models to improve the contextual relevance and accuracy of language models for various tasks, such as question answering, document retrieval, and interactive agents.

· API Integration: Integrate LLMs with external APIs (OpenAI, Hugging Face, etc.) for diverse applications, ensuring smooth interoperability across systems.

· Computer Vision: Work with computer vision models and techniques, applying image processing and deep learning approaches to solve vision-related tasks (e.g., object detection, image generation, or scene understanding). Integrate computer vision capabilities with LLM-based applications for multi-modal solutions.

· Traditional Machine Learning: Apply traditional machine learning algorithms (e.g., Random Forest, SVM, Logistic Regression) where appropriate. Develop end-to-end ML solutions for predictive analytics, classification, regression, and clustering tasks in both structured and unstructured data contexts.

Required Qualifications:

· Educational Background: Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related fields.

· Experience with LLMs: 2+ years of experience working with large language models (e.g., GPT, BERT, T5, etc.) and related AI/NLP technologies.

· Langchain Expertise: Hands-on experience building AI workflows using Langchain for enhanced integration between models and external data sources.

· Vector Database Knowledge: Familiarity with vector databases (e.g., Pinecone, FAISS, Weaviate) and vector search techniques for high-dimensional data retrieval.

· NLP and Transformer Models: Strong understanding of NLP concepts and transformer-based architectures for language modeling, text processing, and embedding generation.

· Programming Languages: Proficiency in Python, with extensive experience using frameworks like TensorFlow, PyTorch, and Hugging Face.

· Cloud Platforms: Experience deploying AI models on cloud platforms (AWS, GCP, Azure) and managing resources for large-scale model training and inference.

· MLOps & Deployment: Practical experience with MLOps practices and tools to manage the lifecycle of machine learning models in production environments.

Preferred Qualifications:

· Experience with Generative AI (text generation, text-to-image models, etc.).

· Hands-on experience with retrieval-augmented generation (RAG) to improve model responses using external knowledge sources.

· Prior experience working with multi-modal models (text, image, audio).

· Familiarity with data tokenization and privacy-preserving techniques.

· Proficiency in computer vision models such as YOLO, ResNet, and segmentation networks.

· Hands-on experience with traditional machine learning algorithms like SVM, Random Forest, or XGBoost.

Soft Skills:

· Strong collaboration and communication skills to work effectively in a multi-disciplinary team environment.

· Ability to solve complex problems with a creative, analytical mindset.

Job Types: Full-time, Permanent

Pay: ₹13,067.69 - ₹80,000.00 per month

Benefits:

• Health insurance
• Provident Fund

Work Location: In person