kedar limbalkar

ML Engineer

https://www.opentalent.in/kedar-limbalkar

ML Engineer with less than a year in AI/ML & Data Science

Member since June 24, 2026

Key Strengths

Strong foundational knowledge in Machine Learning, Deep Learning, and NLP, as evidenced by coursework and project work.
Practical experience with Generative AI and LLMs, including LangChain, LangGraph, Llama 3.3, FAISS, and RAG, which is highly relevant for an ML Engineer role.
Demonstrated ability to build and deploy ML/DL models, including MLOps practices like Docker deployment and Streamlit.
Experience in Computer Vision with CNNs, TensorFlow, Keras, and OpenCV.
Proficiency in Python and related ML/DL libraries (Pandas, NumPy, Scikit-learn, XGBoost, Keras, PySpark, Matplotlib, Seaborn).

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's project diversity (Conversational AI, Emotion Recognition) and academic background (MS in CS with ML/AI focus, B.Tech in CE) suggest a strong interest and alignment with an ML Engineer role. Participation in a computing intelligence olympiad and technical council indicates initiative and engagement. However, without psychometric test results, a deeper cultural fit analysis is limited.

Soft Skills & Operational Fit

The resume indicates involvement in coordinating technical events and collaborating with product/engineering teams, suggesting potential for teamwork and organizational skills. However, without specific psychometric or English test results, a comprehensive assessment of soft skills and operational fit is not possible.

AI is analyzing your overall score…

Identifying your key strengths…

Evaluating your skill match against the job requirements…

Assessing your cultural and operational fit

About

Machine Learning Engineer and Data Scientist with hands-on expertise in deep learning, NLP, LLMs, computer vision, and end-to-end ML pipelines. Proficient in RAG workflows, model fine-tuning, prompt engineering, MLOps, and Docker deployment. Proven track record of building and deploying production-grade AI systems with measurable accuracy and latency improvements.

Top Skills

Computer VisionTransformer Models

Skills

PythonPandasNumpyScikit LearnXgboostKerasPysparkMatplotlibSeabornSQLTensorFlowCNNComputer VisionFeature EngineeringStatistical AnalysisLangchainLanggraphFaissHugging FacePrompt EngineeringModel Fine TuningRagTransformer ModelsSemantic Searchmulti-agent-systemsNatural Language Processingtext-classificationSentiment AnalysisWord EmbeddingsDockerGitGithubCiCdStreamlitOpencvRest ApiApache AirflowETL PipelinesData ModelingData WranglingData VisualizationedaMysqlBigQuerySnowflake

Key Strengths

Strong foundational knowledge in Machine Learning, Deep Learning, and NLP, as evidenced by coursework and project work.
Practical experience with Generative AI and LLMs, including LangChain, LangGraph, Llama 3.3, FAISS, and RAG, which is highly relevant for an ML Engineer role.
Demonstrated ability to build and deploy ML/DL models, including MLOps practices like Docker deployment and Streamlit.
Experience in Computer Vision with CNNs, TensorFlow, Keras, and OpenCV.
Proficiency in Python and related ML/DL libraries (Pandas, NumPy, Scikit-learn, XGBoost, Keras, PySpark, Matplotlib, Seaborn).

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit