The Role
Build and deploy scalable AI/ML and LLM applications, including conversational agents, RAG pipelines, vector search systems, agentic workflows, and model-serving APIs. Responsibilities include prompt engineering, model fine-tuning and evaluation, data preprocessing, feature engineering, enterprise integrations, and performance optimization. The role also involves architecture contributions, code reviews, mentoring, documentation, and collaboration with data scientists, product owners, and solution architects.
Summary Generated by Built In
Job Description: Senior AI/ML Engineer (2–5 Years Experience)
Location: Hyderabad, India
Employment Type: Full-Time
Experience Required: 2–5 Years
About the Role
We are looking for a skilled Senior AI/ML Engineer with hands-on experience in building, deploying, and optimizing AI/ML and LLM-based applications. The ideal candidate should have strong Python expertise, experience with LLM frameworks, and the ability to design scalable AI solutions for enterprise use cases. You will collaborate with cross-functional teams to build agentic AI workflows, RAG pipelines, vector search systems, and production-grade AI applications.
Key Responsibilities
AI/LLM Engineering
- Design, develop, and deploy end-to-end LLM-based applications, conversational agents, and automation workflows.
- Build prompt engineering strategies, evaluation workflows, and structured prompting for enterprise use cases.
- Implement RAG (Retrieval Augmented Generation) systems using vector databases such as Pinecone, FAISS, or ChromaDB.
- Fine-tune, evaluate, and optimize models (open-source or API-based LLMs).
ML Engineering & Model Development
- Build and deploy ML models for classification, NLP, prediction, and document processing.
- Work on data preprocessing, feature engineering, and model optimization.
- Integrate ML/LLM workflows using microservices, APIs, webhooks, and automation pipelines.
Architecture & Integrations
- Work with Python frameworks (FastAPI/Flask) to build scalable model-serving endpoints.
- Integrate AI systems with enterprise platforms, databases, and internal applications.
- Contribute to architecture decisions for LLM apps, agentic systems, and AI microservices.
Collaboration & Delivery
- Collaborate with data scientists, product owners, and solution architects to deliver AI-powered features.
- Review code, mentor junior engineers, and set technical standards for AI development.
- Prepare documentation, reusable components, and best practices for AI implementation.
Required Skills
Core Skills
- Strong proficiency in Python (Pandas, NumPy, FastAPI/Flask, Async programming preferred).
- Hands-on experience with LLMs (OpenAI, Llama, Mistral, Claude, Qwen, etc.).
- Experience with LangChain, LlamaIndex, or Hugging Face.
- Strong understanding of RAG pipelines, embeddings, and vector search.
- Practical experience with vector databases: Pinecone, FAISS, Weaviate, or ChromaDB.
- Knowledge of REST APIs, JSON integrations, token-based authentication.
- Experience with Git, Docker, CI/CD workflows.
ML & Data Skills
- Experience with NLP, classical ML, text preprocessing, and supervised learning.
- Ability to evaluate model performance and optimize latency & accuracy.
- Understanding of data pipelines and ETL workflows.
Cloud & DevOps
- Experience with at least one cloud platform (AWS/Azure/GCP).
- Familiarity with Kubernetes, model deployment, or container orchestration (nice to have).
Nice-to-Have Skills
- Experience with agentic AI frameworks (AutoGen, CrewAI, LangGraph, Swarm, etc.).
- Exposure to MLOps tools (MLflow, Airflow, Weights & Biases).
- Knowledge of front-end AI app integration (Streamlit, Gradio, or React).
- Understanding of AI security, governance, and responsible AI principles.
Qualifications
- Bachelor’s or Master’s degree in Computer Science, AI/ML, Data Science, or related field.
- 3–5 years of hands-on experience in AI/ML or LLM engineering.
- Previous project or product development experience in NLP/LLM highly preferred.
What We Offer
- Opportunity to work on cutting-edge AI/LLM technologies.
- Exposure to enterprise-grade AI architectures, RAG systems, and agentic workflows.
- Mentorship from senior architects and a collaborative engineering culture.
- Innovation-driven environment with continuous learning opportunities.
Skills Required
- Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or a related field
- 3-5 years of hands-on experience in AI/ML or LLM engineering
- Strong proficiency in Python, including Pandas and NumPy
- Hands-on experience with LLMs such as OpenAI, Llama, Mistral, Claude, or Qwen
- Experience with LangChain, LlamaIndex, or Hugging Face
- Strong understanding of RAG pipelines, embeddings, and vector search
- Practical experience with vector databases such as Pinecone, FAISS, Weaviate, or ChromaDB
- Knowledge of REST APIs, JSON integrations, and token-based authentication
- Experience with Git, Docker, and CI/CD workflows
- Experience with NLP, classical machine learning, text preprocessing, and supervised learning
- Ability to evaluate model performance and optimize latency and accuracy
- Understanding of data pipelines and ETL workflows
- Experience with at least one cloud platform: AWS, Azure, or GCP
- Async programming experience
- Familiarity with Kubernetes, model deployment, or container orchestration
- Experience with agentic AI frameworks such as AutoGen, CrewAI, LangGraph, or Swarm
- Exposure to MLOps tools such as MLflow, Airflow, or Weights & Biases
- Knowledge of Streamlit, Gradio, or React for AI application integration
- Understanding of AI security, governance, and responsible AI principles
- Previous project or product development experience in NLP or LLMs
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The Company
What We Do
AidenAI builds and operates enterprise AI solutions, including AI agents and its unified agentic platform for modernizing legacy technology, transforming business logic and data, validating outcomes, and accelerating migration. It supports organizations from AI strategy through integration and ongoing operations, while providing tools to build, deploy, govern, and measure enterprise agents. Its services are intended to move AI beyond experimentation and embed it into business operations.
