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AI Engineer with 1+ years in Generative AI and LLMs.
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Assessing your cultural and operational fit
AI Engineer specializing in production-level Generative AI, LLMs, and agentic workflows, with hands-on experience building scalable RAG systems, custom fine-tuning platforms, and cloud-native AI pipelines on Azure. Proficient in MLOps/LLMOps, responsible AI alignment via RLHF, and high-performance backend APIs.
Maharashtra Institute of Technology, Aurangabad
B.Tech · Artificial Intelligence and Data Science
August 1, 2022 – August 1, 2025
Sutherland (formerly ATMECS)
AI/ML Engineer
July 1, 2025 – Present
Hyderābād, Telangana, India
Sutherland (formerly ATMECS)
AI Intern
July 1, 2024 – July 1, 2025
Hyderābād, Telangana, India
ResearchOS
January 1, 2025 – January 1, 2025
Architected a 5-stage CrewAI pipeline to orchestrate autonomous multi-agent workflows across specialized nodes. Engineered a FastAPI backend via Groq (Llama 3.3) with async task loops and Pydantic for real-time tracking. Integrated ChromaDB vector memory with Tavily API for dynamic web scraping and retrieval optimization. Built automated asset generation engines using ReportLab and python-pptx for instant PDF and slide delivery.
View ProjectTraceMind-AI
January 1, 2025 – January 1, 2025
Architected a real-time voice-enabled AI assistant with low-latency STT and TTS streaming pipelines. Integrated OpenTelemetry (OTel) across the AI lifecycle for distributed tracing, streaming debug, and latency monitoring implementing LLMOps best practices for production observability. Tracked real-time token consumption, execution errors, and usage costs via OTel LLM observability metrics.
View ProjectGoogle Cloud Professional Machine Learning Engineer
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects (TraceMind-AI, ResearchOS) demonstrate initiative and a passion for cutting-edge AI technologies, aligning well with an innovative and research-driven culture. The experience with multi-agent systems, real-time AI assistants, and LLMOps best practices shows a breadth of skills and a willingness to tackle complex challenges. The role at Sutherland (formerly ATMECS) as an AI/ML Engineer and Intern indicates practical industry experience, suggesting adaptability to corporate environments. The Google Cloud Professional Machine Learning Engineer certification, though future-dated, shows a commitment to continuous learning and professional development.
Soft Skills & Operational Fit
The candidate's project descriptions and experience highlight a proactive approach to problem-solving and a strong focus on architectural design and optimization. The emphasis on MLOps, observability, and responsible AI suggests an operational mindset suitable for production environments. The academic achievements indicate strong analytical capabilities and a drive for excellence.