Tower Research Capital is Hiring AI/ML Intern | Gurgaon | Students (6-Month, Jan 2027)

Job Description

Tower Research Capital's AI/ML internship is worth flagging as one of the more technically demanding internship listings on this site — despite being labeled "Intern," the qualifications read closer to what a mid-level ML engineer might be expected to know, not a typical student internship. Tower is a systematic trading and quantitative finance firm, meaning this internship sits inside a 𝐡𝐢𝐠𝐡-𝐩𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞, 𝐥𝐨𝐰-𝐥𝐚𝐭𝐞𝐧𝐜𝐲 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐜𝐮𝐥𝐭𝐮𝐫𝐞 rather than a typical product company — the responsibilities explicitly connect to their broader engineering work in FPGA technology and low-latency trading infrastructure, giving this a genuinely different flavor from a standard AI internship at a SaaS company.

What makes this listing particularly distinctive right now is its 𝐞𝐱𝐩𝐥𝐢𝐜𝐢𝐭 𝐟𝐨𝐜𝐮𝐬 𝐨𝐧 𝐚𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐭𝐨𝐨𝐥𝐢𝐧𝐠 — building multi-agent systems, working with Model Context Protocol (MCP), and using agentic coding assistants like Claude Code to accelerate development. This is a genuinely cutting-edge skill set that's still uncommon even among experienced engineers, let alone students; the listing states expertise here as 𝐫𝐞𝐪𝐮𝐢𝐫𝐞𝐝, not preferred, which is an unusually high bar for an internship posting and signals Tower expects candidates who've already been experimenting seriously with these newer AI development paradigms, not just traditional ML model training.

Skills-wise, the role blends two distinct competencies: modern agentic AI/LLM tooling (multi-agent orchestration, MCP integration) and traditional ML engineering (TensorFlow/PyTorch model training, MLOps for production deployment). For most students, the traditional ML side is more likely to be covered through coursework or standard project work, while the agentic AI/multi-agent systems side is genuinely emerging technology — hands-on experience here is far rarer and would be a strong differentiator, likely gained through personal projects or recent hackathon work rather than any formal curriculum.

𝐎𝐮𝐫 𝐓𝐚𝐤𝐞:

- 𝐆𝐨𝐨𝐝 𝐟𝐨𝐫: students from top universities with genuine hands-on experience in both traditional ML (PyTorch/TensorFlow) and modern agentic AI tooling (multi-agent systems, MCP, Claude Code or similar), who are specifically interested in quantitative finance/trading technology

- 𝐍𝐨𝐭 𝐢𝐝𝐞𝐚𝐥 𝐟𝐨𝐫: those without recent, hands-on agentic AI project experience — this isn't a role to apply to hoping to learn multi-agent systems on the job; the listing frames it as an existing requirement

- 𝐒𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐝𝐢𝐟𝐟𝐢𝐜𝐮𝐥𝐭𝐲: very high — the combination of a "top university" qualification bar, required expertise in emerging agentic AI tooling, and traditional ML fundamentals makes this one of the more competitive internship listings covered on this site

- 𝐂𝐚𝐫𝐞𝐞𝐫 𝐭𝐫𝐚𝐣𝐞𝐜𝐭𝐨𝐫𝐲: Tower's internship programs at quant trading firms commonly convert into full-time offers; this specific combination of skills (agentic AI + traditional ML + finance exposure) is increasingly valuable across both quant finance and broader AI engineering roles

𝐂𝐨𝐦𝐩𝐞𝐧𝐬𝐚𝐭𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐞𝐱𝐭: Tower hasn't disclosed a stipend for this role. Internships at top-tier quantitative trading firms in India typically offer some of the highest stipends in the market — often significantly above standard tech internships — though without a stated figure here, candidates should confirm compensation directly during the interview process rather than assuming a specific number.

𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: Given that "expertise" in agentic coding tools and multi-agent systems is stated as required, your application should lead with concrete, recent examples — a project where you built a multi-agent workflow, integrated with MCP, or used an agentic coding assistant to build real tooling — rather than general ML project experience alone; given how new this skill area is, a specific, well-documented example is likely to matter more here than at almost any other listing on this site, since few applicants will have genuine hands-on depth in this specific area yet.

Roles & Responsibilities

The responsibilities split between emerging agentic AI development and traditional ML engineering — both areas are treated as core to the role, not one primary focus with the other as a side task.

- Build and evaluate multi-agent systems, including agent orchestration and tool-calling, integrating with frameworks like Model Context Protocol (MCP)

- Implement agentic coding tools (e.g., Claude Code) to accelerate development and automate internal tooling

- Develop and implement statistical and machine learning algorithms for business and technical needs

- Contribute to MLOps practices for deploying, monitoring, and maintaining production ML models

- Analyze model performance through experiments and testing, contributing to optimization and fine-tuning

Qualifications & Eligibility

Notably specific on both timing and institutional pedigree — this internship has a fixed start date and an explicit "top university" qualifier, both worth confirming fit against before applying.

- Bachelor's, Master's, or PhD (ongoing or completed) from a top university

- Available for a 6-month in-office internship starting January 2027

- Required: expertise in agentic AI tooling — coding assistants (Claude Code or similar), multi-agent systems, MCP/agent skills integration

- Experience training and deploying ML models using TensorFlow, PyTorch, or similar

- Strong Python, with hands-on Linux, SQL, Git, and Bash scripting

- Finance domain background or experience is a plus, not required

Skills Required

The required skill combination is unusually broad and technically deep for an internship — spanning emerging AI tooling, traditional ML frameworks, and core software engineering fundamentals.

- Agentic AI tooling: multi-agent systems, MCP, agentic coding assistants (required)

- ML frameworks: TensorFlow, PyTorch, or similar (required)

- MLOps: model deployment, productionization, lifecycle management

- Python, Linux, SQL, Git, Bash scripting

- Finance domain familiarity (plus, not required)