Developer III - Software Engineering -AI ML

Independently develops error free code with high quality validation of applications guides other developers and assists Lead 1 - Software Engineering

Outcomes:

1. Understand and provide input to the application/feature/component designs; developing the same in accordance with user stories/requirements.
2. Code debug test document and communicate product/component/features at development stages.
3. Select appropriate technical options for development such as reusing improving or reconfiguration of existing components.
4. Optimise efficiency cost and quality by identifying opportunities for automation/process improvements and agile delivery models
5. Mentor Developer 1 - Software Engineering and Developer 2 - Software Engineering to effectively perform in their roles
6. Identify the problem patterns and improve the technical design of the application/system
7. Proactively identify issues/defects/flaws in module/requirement implementation
8. Assists Lead 1 - Software Engineering on Technical design. Review activities and begin demonstrating Lead 1 capabilities in making technical decisions

Measures of Outcomes:

1. Adherence to engineering process and standards (coding standards)
2. Adherence to schedule / timelines
3. Adhere to SLAs where applicable
4. Number of defects post delivery
5. Number of non-compliance issues
6. Reduction of reoccurrence of known defects
7. Quick turnaround of production bugs
8. Meet the defined productivity standards for project
9. Number of reusable components created
10. Completion of applicable technical/domain certifications
11. Completion of all mandatory training requirements

Outputs Expected:

Code:

1. Develop code independently for the above

Configure:

1. Implement and monitor configuration process

Test:

1. Create and review unit test cases
   scenarios and execution

Domain relevance:

1. Develop features and components with good understanding of the business problem being addressed for the client

Manage Project:

1. Manage module level activities

Manage Defects:

1. Perform defect RCA and mitigation

Estimate:

1. Estimate time
   effort
   resource dependence for ones own work and others work
   including modules

Document:

1. Create documentation for own work as well as perform peer review of documentation of others work

Manage knowledge:

1. Consume and contribute to project related documents
   share point
   libraries and client universities

Status Reporting:

1. Report status of tasks assigned
2. Comply with project related reporting standards/process

Release:

1. Execute release process

Design:

1. LLD for multiple components

Mentoring:

1. Mentor juniors on the team
2. Set FAST goals and provide feedback to FAST goals of mentees

Skill Examples:

1. Explain and communicate the design / development to the customer
2. Perform and evaluate test results against product specifications
3. Develop user interfaces business software components and embedded software components 5 Manage and guarantee high levels of cohesion and quality6 Use data models
4. Estimate effort and resources required for developing / debugging features / components
5. Perform and evaluate test in the customer or target environment
6. Team Player
7. Good written and verbal communication abilities
8. Proactively ask for help and offer help

Knowledge Examples:

• Appropriate software programs / modules
  1. Technical designing
  2. Programming languages
  3. DBMS
  4. Operating Systems and software platforms
  5. Integrated development environment (IDE)
  6. Agile methods
  7. Knowledge of customer domain and sub domain where problem is solved
  
  

  Job Description - ho we are? You will be part of the ML R&D team which works on some really cool problems and (sometimes not-so-cool :-) problems). We apply cutting edge ML to solve hard problems like Document Understanding (or Document Al). We have a solution in production which is on par with the industry players in multiple facets. We reason things from the 1 st principles, or we build on top of existing things as the problem dictates. We as a team push the boundary of ML and constantly work on techniques to solve problems with no or little training data. We are a very flat org; everyone is technically sound and very collaborative. Your typical day would involve creating datasets from the scratch or run multiple iterations of feature engineering or come up with a great representation learning technique or conceptualize a nifty transfer learning solution, fit a model to the data and package the model to serve in batch or in online fashion. Who we are looking for? We are flexible and are looking for the top talent ideally with 3-5 years industry experience or 1-2 years academic experience. Programming Experience: Ninja Programmer in one of the following Python/ R. Applied ML Experience: o Problem framing: Strong problem framing skills: Say, when to go with Supervised or self-supervised or RL setting. o Data wrangling skills: Experience in techniques like Weak/Distant Supervision and Pseudo labelling) Strong EDA, data preparation and labelling skills Strong data augmentation skills o From the scratch learning: Strong experience in end to end modelling in (ML vs DL vs RL), Experience in Single models vs Ensembles vs Mixture of experts. Mathematical understanding of some Mathematical Induction, Tree Induction, DL and other optimization algorithms like SGD. o Transfer Learning Experience in N-shot learning (or its variants) Fine tuning skills UST Global Ltd 1 SmartOps Strategic R&D o ML/DL Verticals: Proven research or industry experience in one of the areas like Time series modelling, Vision, NLP, RL. A GitHub portfolio with original ML repos. A Kaggle portfolio with decent leader board positions Papers: Original 1 st author papers in reputed ML journals or conferences. Patents: Al or Automation specific patent is a good to have Experience with ML/DL libraries TensorFlow or PyTorch MLOps: Experience in running machine learning experiments with any one of the above machine learning libraries. Good to have is any one of the following: Kubeflow, Mlflow or Airflow or SparkML Deploying machine learning solutions into production. Model Serving TFServe, Seldon, Custom serving. Interactive, batching and streamed serving. Optimizing solutions for performance and scalability. Data engineering, i. e. ensuring a good data flow between database and backend systems. Implementing custom machine learning code (like custom implementation of existing algorithms like SGD) when required Coming up with our own DNN architectures when required Good to have: Computer science or IT background Good to have: Exposure to statistics and probability. Good to have: Experience in running dockerized code, we are a Kubernetes shop