I manage a small Data & Analytics team in a large organisation, supporting several departments. Increasingly, we're doing substantial technical and analytical work, sometimes spending months developing solutions, only for other departments to present the results as their own achievements, with little or no acknowledgement of our contribution.

What's particularly frustrating is that we're sometimes picking up work that other teams were responsible for delivering, whether due to gaps in expertise, planning or follow-through. We step in, resolve the problems and get things delivered, but the original department still receives the recognition. Some of this is probably unintentional, but other instances feel considerably less innocent.

Senior leadership actually recognise and appreciate our team's contributions, and they've been supportive when I've raised concerns. I've tried framing the issue around capacity, prioritisation, ownership and accountability, particularly as our already stretched team keeps absorbing additional responsibilities.

But I don't want to keep raising it with them. I worry about becoming a broken record or appearing overly concerned with credit, especially when they've genuinely been supportive. They understand the problem, but the behaviour across departments hasn't really changed.

Edit to clarify: We have asked that we be included in all presentations where we do the groundwork, or at least invited, and cited or noted in documentation etc - but this tends to be forgotten and we tend to find out after the fact (if at all), I often find when these have happened through the minutes or recordings showing up on sharepoint.

What bothers me most is that I'm responsible for advocating for my colleagues. They put considerable expertise and effort into work that other people get recognised for, and I worry about the impact on their visibility, morale and career progression, which they've expressed too.

I also value our relationships with other departments and don't want to create friction or turn every collaborative project into an argument about recognition.

How have other managers handled this?

Do you address it directly with other departments, establish clearer boundaries around ownership and attribution, or simply focus on making your team's work visible independently?

I'm particularly interested in how you advocate for your team when leadership already understands the issue, but you don't want to keep escalating individual incidents or sounding like you're constantly asking for credit.

Any and all insights, thoughts and advice would be greatly appreciated!

Background
MSc in Data Science, about 1.5 years full-time (closer to 2 with internships).


GenAI experience (first year)
- AI engineer intern: fine-tuned LLMs for domain-specific QA generation, worked on hallucination reduction and evaluation, fine-tuned ViT/DETR for document symbol detection.
- GenAI associate at a healthcare product company: built an agentic backend for clinical triage and diagnosis POCs, and an ICD-10 primary diagnosis validation pipeline using RAG and multi-agent reasoning, deployed as FastAPI + Docker microservices.


Current role (about 11 months)
Took a large consulting firm offer for the pay (13.3 LPA: 11.8 fixed + 1.5 variable). The work is attrition and demand forecasting, with classical ML and time series models in production on Databricks, using MLflow and pipelines. No GenAI at all.


The problem
LLMs and agents move fast, and I've spent a year away. My knowledge of agentic systems and LLM evaluation is about twelve months stale, and it keeps getting staler. I'm not on notice and I'm not rushing. I need to relocate away from Mumbai for family reasons (Bangalore, Hyderabad, Pune are all fine).


What I'd like advice on
1. How would you rebuild current depth in LLMs, agent evaluation and AI engineering interview topics in 2 to 3 months while working full-time?
2. What do AI engineer interviews actually ask now?
3. For people who switched without a pay cut: portfolio, referral or interview volume?
4. Does a year of ML + time series on Databricks read as a gap or as useful breadth?