Machine Learning Engineer

Roles & Responsibilities

1. 

Machine Learning Development

2. 

Deep Learning & NLP

3. 

Generative AI & LLM Engineering

4. 

MLOps & Production Deployment

5. 

Data Engineering & Infrastructure

• • Design and implement ML solutions for:
    • Classification, regression, clustering
    • Anomaly detection
    • Recommendation systems
    • Time-series forecasting
  • Perform feature engineering and advanced data preprocessing.
  • Implement
    • Cross-validation strategies
    • Hyperparameter tuning (Grid, Random, Bayesian)
    • Model evaluation pipelines
  • Apply ensemble methods such as Boosting, Bagging, and Stacking.
  • Optimize models for inference speed, memory usage, and scalability.
  • Develop and fine-tune deep learning models:
    • CNNs, RNNs, LSTMs
    • Transformer-based architectures
  • Build NLP pipelines:
    • Tokenization
    • NER
    • Semantic similarity
    • Text classification
  • Fine-tune pretrained transformer models for domain-specific applications.
  • Implement attention mechanisms and embedding pipelines.
  • Integrate Large Language Models into applications.
  • Build:
    • Retrieval-Augmented Generation (RAG) systems
    • Prompt chaining pipelines
    • Multi-step reasoning workflows
  • Perform:
    • Supervised fine-tuning
    • LoRA / QLoRA-based parameter-efficient fine-tuning
    • Instruction tuning
  • Work with:
    • Embeddings
    • Vector similarity search
    • Context management strategies
  • Implement:
    • Hallucination mitigation techniques
    • Output validation & guardrails
    • Token usage optimization
  • Optimize LLM inference using:
    • Quantization
    • Model distillation
    • Caching strategies
  • Deploy models using REST APIs (FastAPI / Flask).
  • Containerize ML services using Docker.
  • Implement CI/CD pipelines for ML workflows.
  • Use model tracking and versioning tools.
  • Implement:
    • Model monitoring
    • Drift detection
    • Automated retraining pipelines
  • Deploy scalable AI systems on cloud infrastructure.
  • Build ETL pipelines for large datasets.
  • Handle structured & unstructured data.
  • Work with distributed systems when required.
    • Design scalable data storage architectures.