About manohar
Building AI systems that do more than generate responses engineering platforms that reason, retrieve, automate, and scale in real enterprise environments. Across AI/ML engineering, data platforms, and cloud-native architecture, I specialize in bridging the gap between advanced AI research and production grade enterprise systems. My work focuses on Agentic AI, LLM orchestration, RAG ecosystems, semantic retrieval, and scalable machine learning infrastructure that can operate reliably at enterprise scale.I have designed intelligent systems leveraging OpenAI, LangChain, LangGraph, Hugging Face, PyTorch, Databricks, Snowflake, Pinecone, Weaviate, Kubernetes, and multi-cloud environments across AWS, Azure, and GCP. From distributed data pipelines to autonomous multi-agent workflows, I enjoy solving problems where AI, infrastructure, and business strategy intersect.What drives me most is building AI that becomes operationally useful not just technically impressive. Whether it's creating context aware enterprise assistants, optimizing ML lifecycle automation, integrating LLMs with enterprise APIs, or designing resilient MLOps ecosystems, I focus on systems that teams can trust, maintain, and scale.Beyond engineering, I actively collaborate with architects, product leaders, and stakeholders to translate complex business challenges into practical AI solutions. I enjoy leading technical discussions, mentoring teams, and driving AI adoption strategies aligned with long-term business impact. I enjoy solving complex business problems using modernizing legacy systems with intelligent automation, and building scalable platforms that create measurable business value.
Key Skills
Experience
Ai Ml Engineer
CurrentCigna Healthcare
I work on enterprise AI initiatives focused on machine learning, Generative AI, and scalable cloud platforms. My role involves turning business challenges into practical AI solutions that can be deployed securely and at scale. I have helped build intelligent applications using LLMs, retrieval-based systems, and modern MLOps practices. I also collaborate across engineering and business teams to design production-ready solutions, streamline model delivery, and improve adoption of AI across the organization. Key focus areas include cloud deployment, AI platform engineering, model lifecycle management, and integrating AI capabilities into enterprise systems.
Machine Learning Engineer
Uber
* Built scalable ingestion pipelines using Kafka and Azure Event Hub for high-volume data streams * Developed ADF + Databricks pipelines integrated with Snowflake for transforming customer and operational data * Designed distributed data processing workflows using Spark for real-time and batch analytics * Developed reusable data integration pipelines and APIs for cross-system data exchange * Implemented data quality checks and anomaly detection using SQL and data modeling techniques * Automated workflows using Azure DevOps pipelines and scripting, improving efficiency * Established governance and metadata management using Azure Purview and Apache Atlas * Built monitoring dashboards using Azure Monitor, Prometheus, and Grafana * Started my career building data solutions, improving reporting processes, and supporting analytics teams with clean, structured data. Worked with cloud tools, scripting, and automation while developing a strong foundation in large-scale data systems. * This experience laid the groundwork for my later transition into AI and machine learning engineering.
Education
University Of North Texas
Masters
Guru Nanak Institutions(gni)
Bachelors
Jntuh College Of Engineering Hyderabad
Bachelors
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Common Questions
What is manohar's expertise?
manohar specializes in Vector Database Engineer, with expertise in agentic ai development, agile methodologies, alteryx, apache kafka, auditing.
Where is manohar located?
manohar is based in united states.
How much experience does manohar have?
manohar has 6+ years of professional experience.
How can I contact manohar?
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