We are seeking a skilled and hands-on AI/ML Engineer with 4 to 6 years of experience to design, build, and optimize our next-generation AI production pipelines. In this role, you won't just be calling APIsyou will be architecting robust Retrieval-Augmented Generation (RAG) systems, optimizing token usage, and implementing rigorous evaluation frameworks to measure performance and cost.

The ideal candidate bridges the gap between classic machine learning excellence and cutting-edge Large Language Model (LLM) orchestration.

Role & responsibilities :

- RAG Pipeline Development: Design, build, and maintain production-grade Retrieval-Augmented Generation (RAG) pipelines.
- Data Preparation & Vectorization: Implement advanced chunking strategies, select optimal text embedding models, and manage vector databases for highly accurate retrieval.
- Search & Retrieval Optimization: Fine-tune the retrieval process using advanced search, hybrid search, and reranking models to ensure the most relevant context is fed to the LLM.
- Core Machine Learning: Apply classic ML techniques where appropriate, including model selection, regression, classification, clustering, and ensemble methods (bagging and boosting).
- System Evaluation & Monitoring: Build internal leaderboards and evaluation frameworks to rigorously track and capture pipeline performance, response accuracy, and token costs.
- Integration & Deployment: Consume and integrate LLMs via robust REST APIs or Model Context Protocol (MCP) based integrations to ensure seamless data flow across applications.

Preferred candidate profile : Generative AI & RAG Orchestration





- Chunking & Embeddings: Deep understanding of semantic, fixed-size, and parent-child chunking strategies alongside modern text embedding models.
- Search Architecture: Hands-on experience with vectorization, vector databases (e.g., Pinecone, Milvus, Qdrant, Chroma), and deploying retriever and reranking algorithms (e.g., Cohere Rerank, Cross-Encoders).
- LLM Utilization & Optimization: Proven track record of consuming LLMs (OpenAI, Anthropic, open-source via Hugging Face) while implementing strict token optimization techniques to control latency and costs.
- Integration Protocols: Experience with API development and integration, including standard RESTful architectures and emerging standards like MCP (Model Context Protocol).

Core Machine Learning

- Solid grasp of foundational ML concepts: Model selection, regression, classification, and clustering.
- Strong hands-on experience with ensemble methods, specifically bagging and boosting (e.g., Random Forest, XGBoost, LightGBM).

Analytics & MLOps

- Experience building analytics dashboards or leaderboards to monitor system performance, output accuracy (e.g., RAGAS framework, TruLens), and financial token metrics.

Experience & Soft Skills

- 46 years of professional experience in an AI/ML engineering role.
- Robust proficiency in Python and standard ML libraries (Scikit-Learn, Pandas, NumPy).
- Experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex) is highly preferred.
- A data-driven mindset with a passion for optimizing both system performance and cloud/API spend.

📌 Artificial Intelligence Engineer (Hyderabad)
🏢 Amerisource Solutions
📍 Hyderabad