Senior Software Engineer- AI/ML

This position demands a robust blend of software engineering mastery and deep machine learning expertise.

1. Core Software Development & Architecture

• Languages:

  Expert proficiency in

  Java

  and

  Kotlin

  (for server-side technologies).

• Distributed Systems:

  Extensive experience building

  large-scale distributed applications and backend services

  .

• Fundamentals:

  Strong command of

  Data Structures, Algorithms, Object-Oriented Programming (OOP), and Software Design/Architecture

  .

• Full Lifecycle Ownership:

  Ability to command the entire software development lifecycle, from design and architecture to coding, testing, deployment, and maintenance.

2. Machine Learning & AI

• End-to-End ML Lifecycle:

  Hands-on experience with the complete ML lifecycle, especially

  model deployment and application at scale

  .

• ML Programming:

  Proven expertise in

  Python

  and key ML libraries like

  scikit-learn, TensorFlow, PyTorch, and Keras

  .

• ML Fundamentals:

  Strong understanding of

  machine learning fundamentals, algorithms, and model evaluation techniques

  .

• ML Ops:

  Practical experience with

  ML Ops tools and best practices

  .

• Performance Metrics:

  Familiarity with key prediction metrics such as

  accuracy, latency, throughput, confidence scores, and data & concept drift

  .

• Bonus/Highly Desirable:

  Experience in

  AI research or industrial recommendation systems

  , familiarity with

  cloud services

  , and knowledge of

  Large Language Models (LLMs) or prompt engineering

  .

3. Data & Big Data Technologies

• Big Data Processing:

  Proven experience designing and operating

  Big Data processing pipelines (Hadoop, Spark)

  .

• NoSQL Databases:

  Experience with

  NoSQL databases or key-value stores (e.g., MongoDB, Redis)

  .

• Data Analysis:

  Proficiency in the

  close examination of data, computation of statistics, and deriving data insights

  .

• Large-Scale Data Handling:

  Ability to work proficiently with

  large-scale data in Hadoop and Spark environments

  .

4. Strategic & Soft Skills

• Technical Leadership:

  Ability to

  develop and execute sound technical strategies

  for complex projects and

  guide junior team members

  .

• Collaboration:

  Strong capability to

  work closely with product managers and partners

  to translate business requirements into technical solutions.

• Innovation:

  Drive for

  developing novel solutions

  to challenging problems.

• Communication:

  Excellent

  communication skills

  .

• Mindset:

  A passion for

  technical excellence

  , a

  "can-do" attitude

  , and a

  willingness to learn and master new technologies

  .

• Statistics:

  Solid foundation in

  core Statistics knowledge

  .