Benefits:

• Unlimited leaves

• Life insurance & private health insurance

• Stock options

• No hierarchy

• Open Salaries

Education Requirements:

• PhD/Master’s/Graduate Degree in Computer Science, Machine Learning, AI, Operational Research, Statistics, or Mathematics. or equivalent.

Experience Requirements:

• Should have experience with a proven track record in client interaction, can collaborate with engineering and business teams and is good at formulation, solving and deploying Data Science models.

• conversant with at least 2 or more of the above areas

• conversant with deep learning and conventional machine learning methods

• expertise in problem solving whilst being hands on and be conversant with coding in Python along with relevant libraries.

Other Requirements:

• requisite theoretical strength

Responsibilities:

• help solve client problems

• conversant with end-to-end data science model life cycle, metrics, and deployment challenges

• conceptualizing, formulating, coding and deploying the solutions to the problem

• conversant with interacting with the clients and be able to make sense of ambiguous problem statements

• building a RAG leveraging open source or commercial LLMs, building a Multiagent system, leveraging Small Language models that could be locally resident, problems in computer vision, speech, Natural Language Processing (NLP), multilingual models, applied statistics, data mining or conventional machine learning models

Benefits:

• Unlimited leaves

• Life insurance & private health insurance

• Stock options

• No hierarchy

• Open Salaries

Experience Requirements:

• Demonstrated experience as a Senior Data Engineer in complex enterprise environments.

• Deep understanding of technology fundamentals and experience with languages like Python, or functional programming languages like Scala

• Demonstrated experience in design and development of big data applications using tech stacks like Databricks, Apache Spark, HDFS, HBase and Snowflake

• Commendable skills in building data products, by integrating large sets of data from hundreds of internal and external sources would be highly critical

• Ability to deliver an application end to end; having an opinion on how your code should be built, packaged and deployed using CI/CD

Other Requirements:

• A nuanced understanding of code quality, maintainability and practices like Test Driven Development

• Understanding of Cloud platforms, DevOps, GitOps, Containers

Responsibilities:

• Collaborate with Data Scientists and Engineers to deliver production quality AI and Machine Learning systems

• Build frameworks and supporting tooling for data ingestion from a complex variety of sources

• Consult with our clients on data strategy, modernising their data infrastructure, architecture and technology

• Model their data for increased visibility and performance

• You will work in short sprints to deliver working software

Benefits:

• Unlimited leaves

• Life Insurance & Private Health insurance paid by Sahaj

• Stock options

• No hierarchy

• Open Salaries

Experience Requirements:

• At least 8+ years experience as a Software Engineer.

• Deep understanding of programming fundamentals and expertise with at least one programming language (functional or object-oriented).

• A nuanced and rich understanding of code quality, maintainability and practices like Test Driven Development.

• Experience with one or more source control and build toolchains.

• Working knowledge of CI/CD will be an added advantage.

Other Requirements:

• Understanding of web APIs, contracts and communication protocols.

• Understanding of Cloud platforms, infra-automation/DevOps, IaC/GitOps/Containers, design and development of large data platforms.

• A maker’s mindset – To be resourceful and have the ability to do things that have no instructions.

Responsibilities:

• Remain fully hands-on and write high-quality, production-ready code that enables smooth deployment of solutions.

• Lead architecture and design decisions, ensuring adherence to best practices in technology choices and system design.

• Utilize DevOps tools and practices to automate and streamline the build and deployment processes.

• Work closely with Data Scientists and Engineers to deliver robust, production-level AI and Machine Learning systems.

• Develop frameworks and tools for efficient data ingestion from diverse and complex sources.