As an AI Software Engineer at S&P Global, you will be responsible for designing and building agentic AI platform components, implementing observability across the AI lifecycle, and translating business problems into agentic AI solutions. You will work closely with product, SMEs, and platform teams to develop and maintain data pipelines, features, and datasets for training, evaluation, grounding, and safety of LLM-based agents.
S&P Global is a leading provider of financial markets data, analytics, and insights. With a presence in over 30 countries, the company serves a diverse range of clients, including financial institutions, corporations, and governments. S&P Global's technology division is responsible for developing and maintaining the company's cutting-edge platforms and tools, including its AI and machine learning capabilities.
Why required: Python is a preferred language for this role due to its extensive use in AI and machine learning applications.
How recruiters evaluate: The recruiter will evaluate your proficiency in Python, including your ability to write clean, efficient, and well-documented code.
Why required: LLM/GenAI fundamentals are essential for this role, as you will be working with large language models and generative AI.
How recruiters evaluate: The recruiter will assess your understanding of LLM/GenAI concepts, including prompting, embeddings, vector search, RAG, and basic agentic patterns.
Why required: Experience with cloud platforms such as AWS, Azure, or GCP is necessary for this role, as you will be working with production systems and data pipelines.
How recruiters evaluate: The recruiter will evaluate your experience with cloud platforms, including your ability to design, deploy, and manage cloud-based systems.
Why required: Observability tools such as OpenTelemetry, Prometheus, Grafana, ELK, etc. are essential for this role, as you will be implementing observability across the AI lifecycle.
How recruiters evaluate: The recruiter will assess your experience with observability tools, including your ability to design and implement monitoring and logging systems.
Why required: Experience working with structured and unstructured data is necessary for this role, as you will be working with data pipelines and datasets.
How recruiters evaluate: The recruiter will evaluate your ability to work with structured and unstructured data, including your experience with SQL, Pandas, Spark, and dbt.
This role is not suitable for freshers, as it requires 2-6 years of experience in software engineering, data engineering, ML engineering, data science, or MLOps roles.
Experienced professionals with a strong background in AI, machine learning, and software engineering are encouraged to apply.
Graduates with a degree in Computer Science, Engineering, Data Science, or equivalent practical experience are eligible to apply.
B.Tech graduates with a degree in Computer Science, Engineering, or equivalent practical experience are eligible to apply.
MCA graduates with a degree in Computer Science, Engineering, or equivalent practical experience are eligible to apply.
Diploma holders with a degree in Computer Science, Engineering, or equivalent practical experience are eligible to apply.
The salary range for this role in the industry is between ₹15 lakhs and ₹30 lakhs per annum.
Freshers can expect a salary range of ₹10 lakhs to ₹15 lakhs per annum.
Experienced professionals can expect a salary range of ₹20 lakhs to ₹40 lakhs per annum.
The salary growth for this role is expected to be around 10-15% per annum, depending on performance and experience.
With 5-7 years of experience, you can move into a senior role, leading teams and working on more complex projects.
With 8-10 years of experience, you can move into a technical lead role, overseeing multiple teams and projects.
With 10+ years of experience, you can move into an engineering manager role, responsible for the overall direction and strategy of the engineering team.
This course provides a comprehensive introduction to LLM/GenAI concepts and applications.
This course covers the basics of observability tools and how to implement them in production systems.
This course provides an introduction to cloud platforms and how to use them for deploying and managing applications.
To succeed in this role, focus on developing your skills in AI, machine learning, and software engineering. Stay up-to-date with the latest developments in the field, and be prepared to work on complex projects and collaborate with cross-functional teams.
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AI Software Engineer
Hyderabad, India; Gurgaon, India
Information Technology
323823
Job Description
About the Role:
Grade Level (for internal use):
10
Key Responsibilities
Design and build agentic AI platform components including agents, tools, workflows, and integrations with internal systems.
Implement observability across the AI lifecycle: tracing, logging, metrics, and evaluation pipelines to monitor agent quality, cost, and reliability.
Translate business problems into agentic AI solutions by collaborating with product, SMEs, and platform teams on data, model, and orchestration requirements.
Develop and maintain data pipelines, features, and datasets for training, evaluation, grounding, and safety of LLM-based agents.
Lead experimentation and benchmarking: Testing of prompts, models, and agent workflows; analyze results and drive iterative improvements.
Implement guardrails, safety checks, and policy controls across prompts, tool usage, access, and output filtering to ensure safe and compliant operation.
Create documentation, runbooks, and best practices; mentor peers on agentic AI patterns, observability-first engineering, and data/ML hygiene.
Core Skills Required
Strong programming experience in Python (preferred) or equivalent languages
Solid understanding of LLM / GenAI fundamentals: prompting, embeddings, vector search, RAG, and basic agentic patterns (tool use, planning, orchestration).
Experience running production systems or data pipelines on AWS / Azure / GCP, using containers, serverless, and managed storage/services.
Hands-on familiarity with observability tools (OpenTelemetry, Prometheus, Grafana, ELK, etc.) across logs, metrics, and traces.
Comfort working with structured and unstructured data; strong SQL plus experience with Pandas / Spark / dbt or similar frameworks.
Ability to reason clearly about reliability, performance, and cost trade-offs.
Strong collaboration and communication skills; ability to translate complex concepts for platform, product, data, security, and compliance teams.
Qualifications
2–6 years of experience in software engineering, data engineering, ML engineering, data science, MLOps roles.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or equivalent practical experience.
Experience with CI/CD, code reviews, and modern engineering best practices.
Nice to Have:
Exposure to agentic AI frameworks (LangChain, LangGraph, OpenAI Agents, etc.)
Experience with LLM observability, eval frameworks, or prior work on production LLM/agent systems.
What We're Looking For
Beyond skills and experience, we want engineers who:
Build for scale: Think like platform builders and design systems that work across teams, not just for today’s use case.
Lead with observability: Instrument first, debug with data, and deliver dashboards that reveal the truth.
Ship safely: Never deploy without guardrails or validations, even if it adds upfront effort.
Make thoughtful trade-offs: Clearly articulate decisions around cost, quality, latency, and reliability.
Own the end-to-end stack: Move comfortably between data pipelines, agent logic, infrastructure, and production monitoring.
Learn through experimentation: Test ideas, study failures, iterate rapidly, and improve continuously.
Communicate with impact: Explain complex AI concepts in simple, business-relevant terms to technical and non-technical stakeholders.
Stay ahead of the curve: Actively explore emerging technologies like LangGraph, agentic frameworks, and new LLM capabilities.
