This position is posted by Jobgether on behalf of a partner company. We are currently looking for a
Machine Learning Hardware Engineer
in
India
.In this role, you will be responsible for designing, developing, and optimizing enterprise-grade retrieval-augmented generation (RAG) systems and AI infrastructure. You will work on end-to-end AI pipelines, integrating large language models with advanced retrieval systems, and ensuring high-performance, scalable solutions in production. The position involves hands-on development in Python and Golang/Rust, deploying solutions on cloud platforms, and implementing hybrid search, embeddings, and semantic ranking strategies. You will collaborate with cross-functional teams, guide other engineers, and implement best practices for AI workflows and RAG architectures. This is a high-impact role for candidates passionate about pushing the boundaries of AI hardware and software integration while working in a fast-paced, innovative environment.
Accountabilities:
- Lead the design and implementation of RAG architectures to ensure reliability, scalability, and low-latency performance
- Develop and optimize multi-stage AI pipelines using LLM orchestration frameworks such as LangChain, LangGraph, or LlamaIndex
- Build high-performance services and APIs in Python and Golang/Rust to support AI workflows, document ingestion, and retrieval processes
- Implement hybrid search strategies, vector embeddings, and semantic ranking to improve contextual accuracy
- Design, iterate, and optimize prompts for domain-specific applications, including few-shot and tool-augmented prompts
- Collaborate with cross-functional teams, mentor engineers, and establish KPIs for RAG pipeline and model performance
Requirements
- 8+ years in software engineering or applied AI/ML, with at least 2+ years focused on LLMs and retrieval systems
- Strong proficiency in Python and Golang or Rust, with experience building high-performance services and APIs
- Expertise in RAG frameworks (LangChain, LangGraph, LlamaIndex) and embedding models
- Hands-on experience with vector databases (Databricks Vector Store, Pinecone, Weaviate, Milvus, Chroma)
- Solid understanding of hybrid search (semantic + keyword) and embedding optimization
- Experience with cloud ML deployment, preferably AWS and Databricks
- Knowledge of LLM fine-tuning (LoRA, PEFT) and knowledge graph integration is a plus
- Strong problem-solving, decision-making, and communication skills for cross-team collaboration
- Bachelor's degree required; Master's or PhD in CS or related fields preferred
Benefits
- Competitive salary and performance-based incentives
- Flexible, remote-first work environment
- Opportunities for professional development, upskilling, and mentorship
- Exposure to cutting-edge AI technologies and frameworks
- Collaborative and innovative team culture
- Access to wellness initiatives and global community programs
Jobgether is a Talent Matching Platform that partners with companies worldwide to efficiently connect top talent with the right opportunities through AI-driven job matching.When you apply, your profile goes through our AI-powered screening process designed to identify top talent efficiently and fairly.🔍 Our AI evaluates your CV and LinkedIn profile thoroughly, analyzing your skills, experience, and achievements.📊 It compares your profile to the job's core requirements and past success factors to determine your match score.🎯 Based on this analysis, we automatically shortlist the 3 candidates with the highest match to the role.🧠 When necessary, our human team may perform an additional manual review to ensure no strong profile is missed.The process is transparent, skills-based, and free of bias — focusing solely on your fit for the role. Once the shortlist is completed, we share it directly with the company that owns the job opening. The final decision and next steps (such as interviews or additional assessments) are then made by their internal hiring team.
Thank you for your interest!
