Details
Skills
Programming: Python, SQL, Bash/Shell Scripting
Machine Learning: PyTorch, TensorFlow, Scikit-learn, XGBoost, Feature Engineering, Supervised & Unsupervised Learning, Model
Evaluation (Precision, Recall, F1, AUC), Experimentation & Model Optimization
Generative AI & LLMs: LLMs, Prompt Engineering, RAG Pipelines, Embedding Pipelines, Fine-tuning (LoRA, PEFT), LLM Evaluation,
LangChain, LlamaIndex, OpenAI API, Anthropic API, AWS Bedrock, Vector DBs (FAISS, Pinecone, Weaviate), Hybrid Search (BM25 +
Vector Search), Guardrails, Hallucination Mitigation
Data Engineering & Pipelines: Apache Spark, Apache Flink, Kafka, ETL Pipelines, Batch & Streaming Systems, Real-time Data
Processing, Large-scale Dataset Handling, Feature Store
MLOps & Production ML: Model Deployment, Model Monitoring, CI/CD for ML, Airflow, MLflow, DVC, Weights & Biases, Experiment
Tracking, Retraining Pipelines, Inference Optimization (Latency/Throughput), Model Versioning
Cloud & Infrastructure: AWS (S3, EC2, Lambda, Kinesis, EKS, SageMaker, Bedrock), Docker, Kubernetes, PostgreSQL, MySQL, Redis,
Elasticsearch, Distributed Systems, Scalable Backend Systems
APIs & Systems: FastAPI, REST, GraphQL, gRPC, Microservices Architecture, Async Programming, High Availability, Fault-Tolerant
Systems
Observability: Prometheus,Grafana, AWS CloudWatch, Logging, Alerting, Latency Analysis, PerformanceOptimization
Security & Compliance: PII Handling, RBAC, IAM, API Security, Secure Model Serving, Data Validation, GDPR, SOC2 Basics, Zero Trust
Soft Skills & Collaboration: Problem Solving, Communication, Cross-functional Collaboration, Ownership & Accountability,
Stakeholder Management, Debugging & Root Cause Analysis
About
Machine Learning Engineer with 5+ years of experience building and deploying production-grade LLM and GenAIsystems,specializing
in multilingual NLP, RAG pipelines, and embedding-based retrieval. Experienced in designing end-to-end ML systems powering realtime, high-throughput applications handling millions of requests daily, improving model accuracy, latency, and reliability at scale.
Strong expertise in Python, PyTorch, Hugging Face, and AWS, with a focus on MLOps, model evaluation, and scalable deployment.
Proven ability to optimize LLM performance through fine-tuning, experimentation, and production monitoring.
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