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Machine Learning Engineer

Codeclouds IT Solutions
Posted on
Codeclouds IT Solutions logo
Experience
2 - 6 yrs
Job Location
Kolkata, India
Vacancy
1
Designation
Machine Learning Engineer
Job Type
ONSITE

Job Description

We are seeking a highly technical Machine Learning Engineer with strong expertise in classical Machine Learning, Deep Learning, and Generative AI systems (LLMs & foundation models). The ideal candidate must have hands-on experience designing end-to-end ML pipelines, implementing LLM-powered systems, optimizing models for production, and deploying scalable AI solutions using modern MLOps practices. This role requires strong mathematical foundations, solid programming skills, system design thinking, and production deployment experience.
Roles & Responsibilities
    1. Machine Learning Development
    • 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.
    2. Deep Learning & NLP
    • 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.
    3. Generative AI & LLM Engineering
    • 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
    4. MLOps & Production Deployment
    • 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.
    5. Data Engineering & Infrastructure
    • Build ETL pipelines for large datasets.
    • Handle structured & unstructured data.
    • Work with distributed systems when required.
    • Design scalable data storage architectures.
    Tools & Technology Stack (Required / Preferred) Programming & Core Libraries
    • Python (mandatory)
    • NumPy
    • Pandas
    • SciPy
    • Scikit-learn
    Deep Learning Frameworks
    • PyTorch
    • TensorFlow / Keras
    NLP & LLM Ecosystem
    • Hugging Face Transformers
    • Sentence Transformers
    • Tokenizers
    • LangChain or LLM orchestration frameworks
    • OpenAI / Anthropic / other LLM APIs
    • RAG pipeline frameworks
    Vector Databases
    • FAISS
    • Pinecone
    • Weaviate
    • Chroma (preferred)
    Databases
    • PostgreSQL
    • MySQL
    • MongoDB
    • Redis
    MLOps & Experiment Tracking
    • MLflow
    • Weights & Biases
    • DVC (preferred)
    Deployment & Infrastructure
    • FastAPI / Flask
    • Docker
    • Kubernetes (preferred)
    • Nginx (preferred)
    • REST / gRPC services
    Cloud Platforms
    • AWS (SageMaker, EC2, S3)
    • Azure ML
    • Google Cloud AI Platform
    Data & Distributed Processing
    • Apache Spark (preferred)
    • Airflow (preferred)
    Version Control & Collaboration
    • Git (branching strategies, pull requests, code reviews)
    • GitHub / GitLab / Bitbucket
    Required Technical Knowledge
    • Strong understanding of:
      • Linear algebra
      • Probability & statistics
      • Optimization algorithms
    • Deep knowledge of:
      • Transformer architecture
      • Attention mechanisms
      • Embedding models
    • Experience with:
      • Model quantization
      • Inference optimization
      • Latency & cost optimization in LLM systems
    • Understanding of Responsible AI:
      • Bias mitigation
      • Fairness
      • Model explainability
    Soft Skills
    • Strong analytical thinking.
    • Clear documentation practices.
    • Ability to translate business problems into ML solutions.
    • Research-oriented and experimentation mindset.
    • Strong English communication skills.
    Preferred Qualifications
    • Experience with multimodal AI (text + image/audio).
    • Experience with diffusion models.
    • Distributed training (multi-GPU).
    • Experience building production-scale AI SaaS products.
    • Contributions to open-source or research publications.
Ideal Candidate Profile
  • Strong ML theoretical foundation.
  • Production-first mindset.
  • Deep hands-on experience with GenAI.
  • System design and scalability oriented.
  • Passionate about cutting-edge AI technologies.
What we offer you
  • Flexible Working
  • Competitive Compensation
  • Insurance Benefits
  • Training & Mentoring
  • Frequent Celebrations
  • Home Office Allowance
  • Paid Leave Benefits
  • Retirement Benefits
  • Partial Course Funding
  • Team Building Activities