Job Description
Overview
Enterprise64 is a U.S.-based technology company that creates digital solutions for startups to Fortune 500 companies in the United States, Europe, and the United Arab Emirates. We are growing rapidly across all our global offices and have career opportunities for tech professionals who are looking to work in a fast-growing company, work in a fun environment, looking to be challenged, grow, and fast-track their careers.Job Overview
We are hiring an AI/ML Engineer with expertise in Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to join our AI-driven team. This role is focused on fine-tuning, optimizing, and deploying Llama 3.3 models, ensuring seamless integration with data pipelines and real-world AI applications. The AI/ML Engineer will collaborate closely with our Senior Data Scientist and Data Engineer to build scalable, high-performance AI-powered solutions, ensuring optimized retrieval and model efficiency.
Key Responsibilities
- Fine-tune and optimize LLMs for production deployment.
- Work with Agentic AI frameworks like LangChain, LangFlow, CrewAI, and AutoGen.
- Work with frameworks like FastAPI, Flask, or Django
- Design and implement embedding-based similarity algorithms for efficient data retrieval.
- Implement Retrieval-Augmented Generation (RAG) techniques to improve AI-driven responses.
- Utilize vector databases (e.g., Weaviate, Qdrant, PGVector, FAISS, Chroma) for AI-powered search and retrieval tasks.
- Develop Python-based AI/ML pipelines and integrate models into real-world applications.
- Collaborate with Data Engineers to integrate AI models with Vector Database /Convenctinal Database for efficient retrieval.
- Optimize AI inference for low latency and high scalability.
- Ensure AI security best practices and mitigate risks like adversarial attacks.
- Stay updated with advancements in LLMs, AI search, and model deployment strategies.
Preferred Qualifications - Strong Python skills for AI/ML development.
- Hands-on experience with LLMs and transformer-based architectures.
- Experience implementing RAG techniques in AI applications.
- Familiarity with embedding models and NLP pipelines.
- Understanding of AI inference optimization and scalability.
- Knowledge of MLOps tools (MLflow, Weights & Biases, DVC) is a plus.
- Experience deploying models using Docker, Kubernetes, or cloud-based services is preferred.
Benefits- Talent upskilling program.
- Provident fund.
- Health insurance.
- Eligible for US H-1 Visa.
- Project-based bonus.
- Leave encashment.
- EOBI
- Maternity leaves/ Paternal WFH
- Religious holidays
- Work from home facility.
- Referral bonus.
- Shared success reward.
- Paid time off.
- Transportation service.
- Loan and advance salary.
