Machine Learning Engineer

Position:

Location:

Experience Level:

Employment Type:

About the Role

Machine Learning (ML) Engineers

Key Responsibilities

For Freshers / Junior Engineers (0–2 Years):

• Work with senior engineers to implement ML models.
• Preprocess and clean datasets for analysis.
• Apply supervised and unsupervised learning techniques on small datasets.
• Test and validate model outputs.
• Learn ML frameworks and contribute to model deployment.

For Mid-Level Engineers (2–6 Years):

• Design, build, and deploy

  end-to-end ML pipelines

  .
• Work with large datasets and ensure data quality.
• Implement algorithms for classification, regression, clustering, and recommendation systems.
• Optimize models for performance, accuracy, and scalability.
• Deploy ML models into production (on

  cloud platforms – AWS, GCP, Azure

  ).
• Collaborate with data engineers, analysts, and product teams.

For Senior Engineers / Leads (7–10+ Years):

• Lead AI/ML projects from research to production.
• Architect and scale ML solutions using

  distributed systems

  .
• Research and experiment with

  deep learning, reinforcement learning, NLP, and computer vision

  .
• Drive

  MLOps practices

  (CI/CD pipelines for ML, model monitoring, retraining).
• Mentor junior engineers and build AI strategy for the company.
• Collaborate with stakeholders to align AI with business goals.

Required Skills

• Strong foundation in

  Mathematics & Statistics

  (linear algebra, probability, optimization).
• Proficiency in

  Python

  (NumPy, Pandas, Scikit-learn).
• Experience with

  ML frameworks

  : TensorFlow, PyTorch, Keras.
• Knowledge of

  data preprocessing, feature engineering, and model evaluation

  .
• Familiarity with

  SQL/NoSQL databases

  and data pipelines.
• Experience with

  cloud ML platforms

  (AWS Sagemaker, GCP AI Platform, Azure ML) preferred.
• For senior roles: strong expertise in

  deep learning, NLP, computer vision, big data (Spark, Hadoop)

  .

Eligibility

• Freshers:

  Solid knowledge of ML basics, Python, and hands-on academic projects.

• Mid-Level:

  2–6 years of proven ML/AI experience in real-world applications.

• Senior-Level:

  7–10+ years of ML/AI expertise with leadership and large-scale deployment exposure.
• Bachelor’s/Master’s in

  Computer Science, Data Science, AI/ML, Statistics, or related fields

  .

Salary Range (Indicative)

• Freshers (0–2 Years):

  ₹4 LPA – ₹7 LPA

• Mid-Level (2–6 Years):

  ₹8 LPA – ₹18 LPA

• Senior-Level (7–10+ Years):

  ₹20 LPA – ₹40 LPA+ (depending on expertise, domain, and company)

Perks & Benefits

• Competitive salary with performance incentives.
• Opportunity to work on cutting-edge AI projects.
• Flexible work modes (remote, hybrid, office).
• Sponsorship for AI/ML certifications and conferences.
• Health and wellness programs.
• Career growth into AI leadership roles.

real-world AI solutions