Generative AI & ML Engineer - GenAI Systems & MLOps

Bhargavi Thoka

Generative AI / Machine Learning Engineer Email: ************@*****.***

linkedin.com/in/askbhargavi Mobile: +1-203-***-****

PROFESSIONAL SUMMARY

Generative AI / Machine Learning Engineer with 3+ years of experience designing and deploying production-grade GenAI systems at enterprise scale. Expert in multi-LLM architectures, RAG pipelines, semantic search, embeddings, and AI evaluation, with demonstrated impact on cost reduction, latency optimization, and system reliability. Strong background in applied ML, scalable data pipelines, and cloud-native MLOps.

CORE SKILLS

Languages: Python, SQL, Java

GenAI & LLMs: GPT-4/4o, Claude, LLaMA, Mistral • Multi-LLM Routing • Prompt Engineering • Tool Calling • RAG • Semantic Search • AI Agents

Frameworks: LangChain, LlamaIndex, Hugging Face, OpenAI SDK

Vector & Retrieval: FAISS, Pinecone, Chroma, OpenSearch • Hybrid Search (BM25 + Vector)

Machine Learning: Feature Engineering, Clustering, Model Evaluation, Experimentation

Data & Platforms: AWS (S3, Glue, Lambda, Redshift, SageMaker), Spark, Kafka, Airflow

MLOps: Docker, Kubernetes, CI/CD, Model Monitoring, Observability

PROFESSIONAL EXPERIENCE

Ford Motor Company — Generative AI / Machine Learning Engineer Jan 2024 – Present

Architected LLM-agnostic, multi-LLM pipelines with dynamic routing across proprietary and open-source models, cutting inference cost ~28%.

Designed and deployed production RAG systems with embedding lifecycle management and hybrid retrieval (BM25 + vector), improving accuracy and relevance by ~22%.

Optimized low-latency inference pipelines using async execution, batching, and caching, reducing p95 latency ~35%.

Built LLM evaluation, monitoring, and guardrails (offline/online metrics, validation, drift detection), reducing GenAI failures by 50%.

Collaborated with product, platform, and security teams to deliver secure, scalable GenAI services aligned with enterprise governance.

NAVJYOTI — Machine Learning Engineer May 2021 – Aug 2022

Built feature engineering and ML-ready data pipelines with Python, SQL, and Spark to accelerate model development.

Supported model training, validation, and evaluation, boosting experimentation speed by ~40% through improved data quality.

Conducted statistical analysis and EDA to detect signal leakage, feature drift, and performance bottlenecks.

NAVJYOTI — Machine Learning / Data Science Intern Nov 2020 – Apr 2021

Conducted data cleaning, feature analysis, and EDA for predictive modeling tasks.

Built dashboards and automated ingestion pipelines to support ML experimentation and stakeholder reporting.

GENAI SYSTEM HIGHLIGHTS

Multi-LLM Routing Engine: Dynamic model selection optimizing cost, latency, and quality for diverse GenAI workloads.

Enterprise RAG Architecture: Semantic search over structured and unstructured data using vector databases and hybrid retrieval.

Embedding Pipelines: Scalable generation, versioning, indexing, and re-ranking of embeddings across evolving datasets.

Evaluation & Guardrails: Automated quality metrics, hallucination detection, and performance monitoring in production.

EDUCATION

Master of Science in Computer Science – University of Bridgeport. GPA: 3.7 / 4.0 2022 – 2023

Bachelor of Technology in Electronics & Communication Engineering – RGUKT GPA: 3.8 / 4.0 2018 – 2022

CERTIFICATIONS

MATLAB Certification – Numerical computing, data analysis, and algorithm development

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